├── .DS_Store ├── .Rbuildignore ├── .fertile_render_log.csv ├── .github ├── .gitignore └── workflows │ ├── R-CMD-check.yaml │ ├── check-standard.yaml │ ├── document.yaml │ └── pkgdown.yaml ├── .gitignore ├── .software-versions.txt ├── CRAN-RELEASE ├── DESCRIPTION ├── LICENSE ├── LICENSE.md ├── NAMESPACE ├── NEWS.md ├── R ├── Indometh.R ├── abm.R ├── blood_storage.R ├── cath.R ├── cdystonia.R ├── covid_testing.R ├── cytomegalovirus.R ├── diabetes.R ├── esoph_ca.R ├── indo_rct.R ├── laryngoscope.R ├── licorice_gargle.R ├── opt.R ├── polyps.R ├── scurvy.R ├── smartpill.R ├── strep_tb.R ├── supraclavicular.R ├── theoph.R └── thiomon.R ├── README.Rmd ├── README.md ├── _pkgdown.yml ├── cran-comments.md ├── data-raw ├── .DS_Store ├── .Rapp.history ├── Cytomegalovirus.Rdata ├── IndoRCT(subclasses1).dta ├── Laryngoscope.Rdata ├── OPT_Study_PersonLevel_Data.RData ├── abm.Rdata ├── abm.dta ├── blood_storage.Rdata ├── cath.Rdata ├── covid_testing.Rdata ├── covid_testing_code_book.csv ├── covid_testing_deid.csv ├── diabetes.Rdata ├── esoph.Rdata ├── esoph_ca.Rdata ├── indo_rct.Rdata ├── indo_rct_pep.dta ├── indometh.Rdata ├── join_data │ ├── birthdays.csv │ ├── demog.csv │ ├── hometowns.csv │ ├── hometowns2.csv │ ├── labs.csv │ ├── sdoh.csv │ └── vitals.csv ├── licorice_gargle.Rdata ├── messy_cirrhosis.Rdata ├── messy_data │ ├── messy_aki.xlsx │ ├── messy_bp.xlsx │ ├── messy_cirrhosis.xlsx │ ├── messy_glucose.xlsx │ ├── messy_infarct.xlsx │ └── messy_uc.xlsx ├── messy_infarction.Rdata ├── polyps.Rdata ├── polyps.csv ├── prep_data.R ├── scurvy.Rdata ├── scurvy.csv ├── smartpill.Rdata ├── strep_tb.Rdata ├── strep_tb.csv ├── supraclavicular.Rdata ├── tb_trial.xlsx ├── theoph.Rdata ├── thiomon.Rdata └── thiomon.csv ├── data ├── Indometh.rda ├── Theoph.rda ├── abm.rda ├── blood_storage.rda ├── cath.rda ├── cdystonia.rda ├── covid_testing.rda ├── cytomegalovirus.rda ├── diabetes.rda ├── esoph_ca.rda ├── indo_rct.rda ├── laryngoscope.rda ├── licorice_gargle.rda ├── opt.rda ├── polyps.rda ├── scurvy.rda ├── smartpill.rda ├── strep_tb.rda ├── supraclavicular.rda └── thiomon.rda ├── docs ├── 404.html ├── LICENSE-text.html ├── LICENSE.html ├── apple-touch-icon-120x120.png ├── apple-touch-icon-152x152.png ├── apple-touch-icon-180x180.png ├── apple-touch-icon-60x60.png ├── apple-touch-icon-76x76.png ├── apple-touch-icon.png ├── articles │ ├── Introduction to the medicaldata package.html │ ├── Introduction to the medicaldata package_files │ │ └── header-attrs-2.9 │ │ │ └── header-attrs.js │ ├── Introduction to the package.html │ ├── Introduction to the package_files │ │ └── header-attrs-2.9 │ │ │ └── header-attrs.js │ ├── Introduction.html │ ├── Introduction_files │ │ ├── header-attrs-2.10 │ │ │ └── header-attrs.js │ │ ├── header-attrs-2.11.3 │ │ │ └── header-attrs.js │ │ ├── header-attrs-2.11 │ │ │ └── header-attrs.js │ │ ├── header-attrs-2.8 │ │ │ └── header-attrs.js │ │ └── header-attrs-2.9 │ │ │ └── header-attrs.js │ ├── index.html │ ├── making_tables.html │ ├── making_tables_files │ │ ├── header-attrs-2.10 │ │ │ └── header-attrs.js │ │ ├── header-attrs-2.11.3 │ │ │ └── header-attrs.js │ │ ├── header-attrs-2.11 │ │ │ └── header-attrs.js │ │ ├── header-attrs-2.8 │ │ │ └── header-attrs.js │ │ ├── tabwid-1.0.0 │ │ │ ├── scrool.css │ │ │ └── tabwid.css │ │ ├── tabwid-1.1.2 │ │ │ └── tabwid.css │ │ └── tabwid-1.1.3 │ │ │ ├── tabwid.css │ │ │ └── tabwid.js │ ├── package_intro.html │ └── package_intro_files │ │ ├── header-attrs-2.8 │ │ └── header-attrs.js │ │ └── header-attrs-2.9 │ │ └── header-attrs.js ├── authors.html ├── bootstrap-toc.css ├── bootstrap-toc.js ├── deps │ ├── bootstrap-5.1.0 │ │ ├── bootstrap.bundle.min.js │ │ ├── bootstrap.bundle.min.js.map │ │ ├── bootstrap.min.css │ │ ├── font.css │ │ └── fonts │ │ │ ├── 1Ptxg8zYS_SKggPN4iEgvnHyvveLxVs9pbCIPrc.woff │ │ │ ├── 1Ptxg8zYS_SKggPN4iEgvnHyvveLxVvaorCIPrc.woff │ │ │ ├── 4iCs6KVjbNBYlgo6ew.woff │ │ │ ├── 4iCs6KVjbNBYlgoKfw7w.woff │ │ │ ├── 4iCv6KVjbNBYlgoCxCvTtA.woff │ │ │ ├── 4iCv6KVjbNBYlgoCxCvjsGyL.woff │ │ │ ├── 6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7nsDQ.woff │ │ │ ├── 6xK1dSBYKcSV-LCoeQqfX1RYOo3qPa7j.woff │ │ │ ├── 6xK3dSBYKcSV-LCoeQqfX1RYOo3aPA.woff │ │ │ ├── 6xK3dSBYKcSV-LCoeQqfX1RYOo3qOK7j.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3i54rAkw.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vAkw.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwlxdo.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zAkw.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwlxdo.woff │ │ │ ├── CSR54z1Qlv-GDxkbKVQ_dFsvWNRevw.woff │ │ │ ├── CSR54z1Qlv-GDxkbKVQ_dFsvaNA.woff │ │ │ ├── CSR64z1Qlv-GDxkbKVQ_TOQ.woff │ │ │ ├── CSR64z1Qlv-GDxkbKVQ_fOAKSw.woff │ │ │ ├── JTURjIg1_i6t8kCHKm45_ZpC7g0.woff │ │ │ ├── JTURjIg1_i6t8kCHKm45_dJE7g0.woff │ │ │ ├── JTUSjIg1_i6t8kCHKm45xW0.woff │ │ │ ├── KFOlCnqEu92Fr1MmEU9fBBc-.woff │ │ │ ├── KFOlCnqEu92Fr1MmEU9vAA.woff │ │ │ ├── KFOlCnqEu92Fr1MmSU5fBBc-.woff │ │ │ ├── KFOlCnqEu92Fr1MmSU5vAA.woff │ │ │ ├── KFOlCnqEu92Fr1MmWUlfBBc-.woff │ │ │ ├── KFOlCnqEu92Fr1MmWUlvAA.woff │ │ │ ├── KFOmCnqEu92Fr1Me5g.woff │ │ │ ├── KFOmCnqEu92Fr1Mu4mxM.woff │ │ │ ├── QGYpz_kZZAGCONcK2A4bGOj8mNhL.woff │ │ │ ├── S6u8w4BMUTPHjxsAXC-s.woff │ │ │ ├── S6u8w4BMUTPHjxswWA.woff │ │ │ ├── S6u9w4BMUTPHh6UVSwiPHw.woff │ │ │ ├── S6u9w4BMUTPHh6UVeww.woff │ │ │ ├── S6u9w4BMUTPHh7USSwiPHw.woff │ │ │ ├── S6u9w4BMUTPHh7USeww.woff │ │ │ ├── S6uyw4BMUTPHjx4wWA.woff │ │ │ ├── S6uyw4BMUTPHvxo.woff │ │ │ ├── UcCO3FwrK3iLTeHuS_fvQtMwCp50KnMw2boKoduKmMEVuFuYMZs.woff │ │ │ ├── UcCO3FwrK3iLTeHuS_fvQtMwCp50KnMw2boKoduKmMEVuI6fMZs.woff │ │ │ ├── UcCO3FwrK3iLTeHuS_fvQtMwCp50KnMw2boKoduKmMEVuLyfMZs.woff │ │ │ ├── XRXV3I6Li01BKof4MQ.woff │ │ │ ├── XRXW3I6Li01BKofA6sKkZQ.woff │ │ │ ├── XRXW3I6Li01BKofAjsOkZQ.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk5hkWVAexg.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk5hkaVQ.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk8ZkWVAexg.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk8ZkaVQ.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0RkyFjWVAexg.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0RkyFjaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsg-1x4gaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsg-1y4k.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsiH0B4gaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsiH0C4k.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4gaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0C4k.woff │ │ │ ├── pe03MImSLYBIv1o4X1M8cc9iB_5p.woff │ │ │ ├── pe0qMImSLYBIv1o4X1M8cfe5.woff │ │ │ └── q5uGsou0JOdh94bfvQlr.woff │ ├── bootstrap-5.1.3 │ │ ├── bootstrap.bundle.min.js │ │ ├── bootstrap.bundle.min.js.map │ │ ├── bootstrap.min.css │ │ ├── font.css │ │ └── fonts │ │ │ ├── 1Ptxg8zYS_SKggPN4iEgvnHyvveLxVs9pbCIPrc.woff │ │ │ ├── 1Ptxg8zYS_SKggPN4iEgvnHyvveLxVvaorCIPrc.woff │ │ │ ├── 4iCs6KVjbNBYlgo6ew.woff │ │ │ ├── 4iCs6KVjbNBYlgoKfw7w.woff │ │ │ ├── 4iCv6KVjbNBYlgoCxCvTtA.woff │ │ │ ├── 4iCv6KVjbNBYlgoCxCvjsGyL.woff │ │ │ ├── 6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7nsDQ.woff │ │ │ ├── 6xK1dSBYKcSV-LCoeQqfX1RYOo3qPa7j.woff │ │ │ ├── 6xK3dSBYKcSV-LCoeQqfX1RYOo3aPA.woff │ │ │ ├── 6xK3dSBYKcSV-LCoeQqfX1RYOo3qOK7j.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3i54rAkw.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vAkw.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwlxdo.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zAkw.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwlxdo.woff │ │ │ ├── CSR54z1Qlv-GDxkbKVQ_dFsvWNRevw.woff │ │ │ ├── CSR54z1Qlv-GDxkbKVQ_dFsvaNA.woff │ │ │ ├── CSR64z1Qlv-GDxkbKVQ_TOQ.woff │ │ │ ├── CSR64z1Qlv-GDxkbKVQ_fOAKSw.woff │ │ │ ├── JTURjIg1_i6t8kCHKm45_ZpC7g0.woff │ │ │ ├── JTURjIg1_i6t8kCHKm45_dJE7g0.woff │ │ │ ├── JTUSjIg1_i6t8kCHKm45xW0.woff │ │ │ ├── KFOlCnqEu92Fr1MmEU9fBBc-.woff │ │ │ ├── KFOlCnqEu92Fr1MmEU9vAA.woff │ │ │ ├── KFOlCnqEu92Fr1MmSU5fBBc-.woff │ │ │ ├── KFOlCnqEu92Fr1MmSU5vAA.woff │ │ │ ├── KFOlCnqEu92Fr1MmWUlfBBc-.woff │ │ │ ├── KFOlCnqEu92Fr1MmWUlvAA.woff │ │ │ ├── KFOmCnqEu92Fr1Me5g.woff │ │ │ ├── KFOmCnqEu92Fr1Mu4mxM.woff │ │ │ ├── QGYpz_kZZAGCONcK2A4bGOj8mNhL.woff │ │ │ ├── S6u8w4BMUTPHjxsAXC-s.woff │ │ │ ├── S6u8w4BMUTPHjxswWA.woff │ │ │ ├── S6u9w4BMUTPHh6UVSwiPHw.woff │ │ │ ├── S6u9w4BMUTPHh6UVeww.woff │ │ │ ├── S6u9w4BMUTPHh7USSwiPHw.woff │ │ │ ├── S6u9w4BMUTPHh7USeww.woff │ │ │ ├── S6uyw4BMUTPHjx4wWA.woff │ │ │ ├── S6uyw4BMUTPHvxo.woff │ │ │ ├── UcCO3FwrK3iLTeHuS_fvQtMwCp50KnMw2boKoduKmMEVuFuYMZs.woff │ │ │ ├── UcCO3FwrK3iLTeHuS_fvQtMwCp50KnMw2boKoduKmMEVuI6fMZs.woff │ │ │ ├── UcCO3FwrK3iLTeHuS_fvQtMwCp50KnMw2boKoduKmMEVuLyfMZs.woff │ │ │ ├── XRXV3I6Li01BKof4MQ.woff │ │ │ ├── XRXW3I6Li01BKofA6sKkZQ.woff │ │ │ ├── XRXW3I6Li01BKofAjsOkZQ.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk5hkWVAexg.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk5hkaVQ.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk8ZkWVAexg.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk8ZkaVQ.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0RkyFjWVAexg.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0RkyFjaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsg-1x4gaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsg-1y4k.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsiH0B4gaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsiH0C4k.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4gaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0C4k.woff │ │ │ ├── pe03MImSLYBIv1o4X1M8cc9iB_5p.woff │ │ │ ├── pe0qMImSLYBIv1o4X1M8cfe5.woff │ │ │ └── q5uGsou0JOdh94bfvQlr.woff │ ├── bootstrap-5.2.2 │ │ ├── bootstrap.bundle.min.js │ │ ├── bootstrap.bundle.min.js.map │ │ ├── bootstrap.min.css │ │ ├── font.css │ │ └── fonts │ │ │ ├── 1Ptxg8zYS_SKggPN4iEgvnHyvveLxVs9pbCIPrc.woff │ │ │ ├── 1Ptxg8zYS_SKggPN4iEgvnHyvveLxVvaorCIPrc.woff │ │ │ ├── 4iCs6KVjbNBYlgo6ew.woff │ │ │ ├── 4iCs6KVjbNBYlgoKfw7w.woff │ │ │ ├── 4iCv6KVjbNBYlgoCxCvTtA.woff │ │ │ ├── 4iCv6KVjbNBYlgoCxCvjsGyL.woff │ │ │ ├── 6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7nsDQ.woff │ │ │ ├── 6xK1dSBYKcSV-LCoeQqfX1RYOo3qPa7j.woff │ │ │ ├── 6xK3dSBYKcSV-LCoeQqfX1RYOo3aPA.woff │ │ │ ├── 6xK3dSBYKcSV-LCoeQqfX1RYOo3qOK7j.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3i54rAkw.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vAkw.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwlxdo.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zAkw.woff │ │ │ ├── 6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwlxdo.woff │ │ │ ├── CSR54z1Qlv-GDxkbKVQ_dFsvWNRevw.woff │ │ │ ├── CSR54z1Qlv-GDxkbKVQ_dFsvaNA.woff │ │ │ ├── CSR64z1Qlv-GDxkbKVQ_TOQ.woff │ │ │ ├── CSR64z1Qlv-GDxkbKVQ_fOAKSw.woff │ │ │ ├── JTUHjIg1_i6t8kCHKm4532VJOt5-QNFgpCtZ6Ew9.woff │ │ │ ├── JTUHjIg1_i6t8kCHKm4532VJOt5-QNFgpCtr6Ew9.woff │ │ │ ├── JTUHjIg1_i6t8kCHKm4532VJOt5-QNFgpCuM70w9.woff │ │ │ ├── KFOlCnqEu92Fr1MmEU9fBBc-.woff │ │ │ ├── KFOlCnqEu92Fr1MmEU9vAA.woff │ │ │ ├── KFOlCnqEu92Fr1MmSU5fBBc-.woff │ │ │ ├── KFOlCnqEu92Fr1MmSU5vAA.woff │ │ │ ├── KFOlCnqEu92Fr1MmWUlfBBc-.woff │ │ │ ├── KFOlCnqEu92Fr1MmWUlvAA.woff │ │ │ ├── KFOmCnqEu92Fr1Me5g.woff │ │ │ ├── KFOmCnqEu92Fr1Mu4mxM.woff │ │ │ ├── QGYpz_kZZAGCONcK2A4bGOj8mNhL.woff │ │ │ ├── S6u8w4BMUTPHjxsAXC-s.woff │ │ │ ├── S6u8w4BMUTPHjxswWA.woff │ │ │ ├── S6u9w4BMUTPHh6UVSwiPHw.woff │ │ │ ├── S6u9w4BMUTPHh6UVeww.woff │ │ │ ├── S6u9w4BMUTPHh7USSwiPHw.woff │ │ │ ├── S6u9w4BMUTPHh7USeww.woff │ │ │ ├── S6uyw4BMUTPHjx4wWA.woff │ │ │ ├── S6uyw4BMUTPHvxo.woff │ │ │ ├── UcCO3FwrK3iLTeHuS_fvQtMwCp50KnMw2boKoduKmMEVuFuYMZs.woff │ │ │ ├── UcCO3FwrK3iLTeHuS_fvQtMwCp50KnMw2boKoduKmMEVuI6fMZs.woff │ │ │ ├── UcCO3FwrK3iLTeHuS_fvQtMwCp50KnMw2boKoduKmMEVuLyfMZs.woff │ │ │ ├── XRXI3I6Li01BKofiOc5wtlZ2di8HDFwmRTA.woff │ │ │ ├── XRXI3I6Li01BKofiOc5wtlZ2di8HDGUmRTA.woff │ │ │ ├── XRXI3I6Li01BKofiOc5wtlZ2di8HDLshRTA.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk5hkWVAexg.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk5hkaVQ.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk8ZkWVAexg.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0Rk8ZkaVQ.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0RkyFjWVAexg.woff │ │ │ ├── memQYaGs126MiZpBA-UFUIcVXSCEkx2cmqvXlWq8tWZ0Pw86hd0RkyFjaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsg-1x4gaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsg-1y4k.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsiH0B4gaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsiH0C4k.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4gaVQ.woff │ │ │ ├── memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0C4k.woff │ │ │ ├── pe03MImSLYBIv1o4X1M8cc9iB_5p.woff │ │ │ ├── pe0qMImSLYBIv1o4X1M8cfe5.woff │ │ │ └── q5uGsou0JOdh94bfvQlr.woff │ ├── data-deps.txt │ └── jquery-3.6.0 │ │ ├── jquery-3.6.0.js │ │ ├── jquery-3.6.0.min.js │ │ └── jquery-3.6.0.min.map ├── docsearch.css ├── docsearch.js ├── favicon-16x16.png ├── favicon-32x32.png ├── favicon.ico ├── index.html ├── link.svg ├── logo.png ├── news │ └── index.html ├── pkgdown.css ├── pkgdown.js ├── pkgdown.yml ├── reference │ ├── abm.html │ ├── blood_storage.html │ ├── cath.html │ ├── cdystonia.html │ ├── covid_testing.html │ ├── cytomegalovirus.html │ ├── diabetes.html │ ├── esoph.html │ ├── esoph_ca.html │ ├── figures │ │ ├── benign_breast.png │ │ ├── hex-medicaldata.png │ │ ├── logo.png │ │ └── malignant_breast.png │ ├── index.html │ ├── indo_rct.html │ ├── indometh.html │ ├── laryngoscope.html │ ├── licorice_gargle.html │ ├── opt.html │ ├── polyps.html │ ├── scurvy.html │ ├── smartpill.html │ ├── strep_tb.html │ ├── supraclavicular.html │ ├── theoph.html │ └── thiomon.html ├── search.json └── sitemap.xml ├── man ├── .DS_Store ├── abm.Rd ├── blood_storage.Rd ├── cath.Rd ├── cdystonia.Rd ├── codebooks │ ├── abm_codebook.Rmd │ ├── abm_codebook.html │ ├── cath_codebook.Rmd │ ├── cath_codebook.html │ ├── cdystonia_codebook.Rmd │ ├── cdystonia_codebook.html │ ├── covid_testing_codebook.Rmd │ ├── covid_testing_codebook.html │ ├── diabetes_codebook.Rmd │ ├── diabetes_codebook.html │ ├── esoph_ca_codebook.Rmd │ ├── esoph_ca_codebook.html │ ├── indo_rct_codebook.Rmd │ ├── indo_rct_codebook.html │ ├── indometh_codebook.Rmd │ ├── indometh_codebook.html │ ├── polyps_codebook.Rmd │ ├── polyps_codebook.html │ ├── scurvy_codebook.Rmd │ ├── scurvy_codebook.html │ ├── strep_tb_codebook.Rmd │ ├── strep_tb_codebook.html │ ├── theoph_codebook.Rmd │ ├── theoph_codebook.html │ ├── thiomon_codebook.Rmd │ └── thiomon_codebook.html ├── covid_testing.Rd ├── cytomegalovirus.Rd ├── description_docs │ ├── abm_desc.Rmd │ ├── abm_desc.html │ ├── cath_desc.Rmd │ ├── cath_desc.html │ ├── cdystonia_desc.Rmd │ ├── cdystonia_desc.html │ ├── covid_desc.Rmd │ ├── covid_desc.html │ ├── diabetes_desc.Rmd │ ├── diabetes_desc.html │ ├── esoph_ca_desc.Rmd │ ├── esoph_ca_desc.html │ ├── indo_rct_desc.Rmd │ ├── indo_rct_desc.html │ ├── indometh_desc.Rmd │ ├── indometh_desc.html │ ├── polyps_desc.Rmd │ ├── polyps_desc.html │ ├── scurvy_desc.Rmd │ ├── scurvy_desc.html │ ├── strep_tb_desc.Rmd │ ├── strep_tb_desc.html │ ├── theoph_desc.Rmd │ ├── theoph_desc.html │ ├── thiomon_desc.Rmd │ └── thiomon_desc.html ├── diabetes.Rd ├── esoph_ca.Rd ├── figures │ ├── benign_breast.png │ ├── hex-medicaldata.png │ ├── logo.png │ └── malignant_breast.png ├── indo_rct.Rd ├── indometh.Rd ├── laryngoscope.Rd ├── licorice_gargle.Rd ├── opt.Rd ├── polyps.Rd ├── scurvy.Rd ├── smartpill.Rd ├── strep_tb.Rd ├── supraclavicular.Rd ├── theoph.Rd └── thiomon.Rd ├── medicaldata.Rproj ├── pkgdown └── favicon │ ├── apple-touch-icon-120x120.png │ ├── apple-touch-icon-152x152.png │ ├── apple-touch-icon-180x180.png │ ├── apple-touch-icon-60x60.png │ ├── apple-touch-icon-76x76.png │ ├── apple-touch-icon.png │ ├── favicon-16x16.png │ ├── favicon-32x32.png │ └── favicon.ico └── vignettes ├── .gitignore ├── Introduction.Rmd └── making_tables.Rmd /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/.DS_Store -------------------------------------------------------------------------------- /.Rbuildignore: -------------------------------------------------------------------------------- 1 | ^CRAN-RELEASE$ 2 | ^Meta$ 3 | ^doc$ 4 | ^pkgdown$ 5 | ^\.covrignore$ 6 | ^.*\.Rproj$ 7 | ^\.Rproj\.user$ 8 | ^\.travis\.yml$ 9 | ^codecov\.yml$ 10 | ^data-raw$ 11 | ^cran-comments\.md$ 12 | ^doc$ 13 | ^docs$ 14 | ^README\.Rmd$ 15 | ^README-.*\.png$ 16 | ^_pkgdown\.yml$ 17 | ^revdep$ 18 | ^\.github$ 19 | ^LICENSE\.md$ 20 | ^\.github/workflows/R-CMD-check\.yaml$ 21 | ^\.github/workflows/pr-commands\.yaml$ 22 | ^\.github/workflows/pkgdown\.yaml$ 23 | ^man/codebooks$ 24 | ^man/description_docs$ 25 | -------------------------------------------------------------------------------- /.fertile_render_log.csv: -------------------------------------------------------------------------------- 1 | path,path_abs,func,timestamp 2 | Seed @ Start,NA,54,2022-08-07T19:11:41Z 3 | package:tidyverse,NA,base::library,2022-08-07T19:11:44Z 4 | package:flextable,NA,base::library,2022-08-07T19:11:45Z 5 | package:tidyverse,NA,base::library,2022-08-07T19:11:46Z 6 | package:flextable,NA,base::library,2022-08-07T19:11:46Z 7 | Seed Before,NA,54,2022-08-07T19:11:46Z 8 | /Users/peterhiggins/Documents/RCode/medicaldata/data-raw/covid_testing_code_book.csv,/Users/peterhiggins/Documents/RCode/medicaldata/data-raw/covid_testing_code_book.csv,readr::read_csv,2022-08-07T19:11:46Z 9 | Seed After,NA,54,2022-08-07T19:11:46Z 10 | package:tidyverse,NA,base::library,2022-08-07T19:11:47Z 11 | package:flextable,NA,base::library,2022-08-07T19:11:47Z 12 | package:medicaldata,NA,base::library,2022-08-07T19:11:47Z 13 | package:janitor,NA,base::library,2022-08-07T19:11:47Z 14 | package:tidyverse,NA,base::library,2022-08-07T19:11:48Z 15 | package:flextable,NA,base::library,2022-08-07T19:11:48Z 16 | package:readxl,NA,base::library,2022-08-07T19:11:48Z 17 | package:here,NA,base::library,2022-08-07T19:11:48Z 18 | package:tidyverse,NA,base::library,2022-08-07T19:11:49Z 19 | package:flextable,NA,base::library,2022-08-07T19:11:49Z 20 | package:tidyverse,NA,base::library,2022-08-07T19:11:50Z 21 | package:flextable,NA,base::library,2022-08-07T19:11:50Z 22 | Seed @ End,NA,54,2022-08-07T19:11:50Z 23 | LAST RENDERED,NA,proj_render,2022-08-07T19:11:50Z 24 | -------------------------------------------------------------------------------- /.github/.gitignore: -------------------------------------------------------------------------------- 1 | *.html 2 | -------------------------------------------------------------------------------- /.github/workflows/R-CMD-check.yaml: -------------------------------------------------------------------------------- 1 | # Workflow derived from https://github.com/r-lib/actions/tree/v2/examples 2 | # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help 3 | on: 4 | push: 5 | branches: [main, master] 6 | pull_request: 7 | branches: [main, master] 8 | 9 | name: R-CMD-check 10 | 11 | jobs: 12 | R-CMD-check: 13 | runs-on: ubuntu-latest 14 | env: 15 | GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} 16 | R_KEEP_PKG_SOURCE: yes 17 | steps: 18 | - uses: actions/checkout@v3 19 | 20 | - uses: r-lib/actions/setup-r@v2 21 | with: 22 | use-public-rspm: true 23 | 24 | - uses: r-lib/actions/setup-r-dependencies@v2 25 | with: 26 | extra-packages: any::rcmdcheck 27 | needs: check 28 | 29 | - uses: r-lib/actions/check-r-package@v2 30 | -------------------------------------------------------------------------------- /.github/workflows/check-standard.yaml: -------------------------------------------------------------------------------- 1 | # Workflow derived from https://github.com/r-lib/actions/tree/v2/examples 2 | # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help 3 | on: 4 | push: 5 | branches: [main, master] 6 | pull_request: 7 | branches: [main, master] 8 | 9 | name: R-CMD-check 10 | 11 | jobs: 12 | R-CMD-check: 13 | runs-on: ${{ matrix.config.os }} 14 | 15 | name: ${{ matrix.config.os }} (${{ matrix.config.r }}) 16 | 17 | strategy: 18 | fail-fast: false 19 | matrix: 20 | config: 21 | - {os: macos-latest, r: 'release'} 22 | - {os: windows-latest, r: 'release'} 23 | - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} 24 | - {os: ubuntu-latest, r: 'release'} 25 | - {os: ubuntu-latest, r: 'oldrel-1'} 26 | 27 | env: 28 | GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} 29 | R_KEEP_PKG_SOURCE: yes 30 | 31 | steps: 32 | - uses: actions/checkout@v3 33 | 34 | - uses: r-lib/actions/setup-pandoc@v2 35 | 36 | - uses: r-lib/actions/setup-r@v2 37 | with: 38 | r-version: ${{ matrix.config.r }} 39 | http-user-agent: ${{ matrix.config.http-user-agent }} 40 | use-public-rspm: true 41 | 42 | - uses: r-lib/actions/setup-r-dependencies@v2 43 | with: 44 | extra-packages: any::rcmdcheck 45 | needs: check 46 | 47 | - uses: r-lib/actions/check-r-package@v2 48 | with: 49 | upload-snapshots: true 50 | -------------------------------------------------------------------------------- /.github/workflows/document.yaml: -------------------------------------------------------------------------------- 1 | # Workflow derived from https://github.com/r-lib/actions/tree/v2/examples 2 | # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help 3 | on: 4 | push: 5 | paths: ["R/**"] 6 | 7 | name: Document 8 | 9 | jobs: 10 | document: 11 | runs-on: ubuntu-latest 12 | env: 13 | GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} 14 | steps: 15 | - name: Checkout repo 16 | uses: actions/checkout@v3 17 | with: 18 | fetch-depth: 0 19 | 20 | - name: Setup R 21 | uses: r-lib/actions/setup-r@v2 22 | with: 23 | use-public-rspm: true 24 | 25 | - name: Install dependencies 26 | uses: r-lib/actions/setup-r-dependencies@v2 27 | with: 28 | extra-packages: any::roxygen2 29 | needs: roxygen2 30 | 31 | - name: Document 32 | run: roxygen2::roxygenise() 33 | shell: Rscript {0} 34 | 35 | - name: Commit and push changes 36 | run: | 37 | git config --local user.name "$GITHUB_ACTOR" 38 | git config --local user.email "$GITHUB_ACTOR@users.noreply.github.com" 39 | git add man/\* NAMESPACE 40 | git commit -m "Update documentation" || echo "No changes to commit" 41 | git pull --ff-only 42 | git push origin 43 | -------------------------------------------------------------------------------- /.github/workflows/pkgdown.yaml: -------------------------------------------------------------------------------- 1 | # Workflow derived from https://github.com/r-lib/actions/tree/v2/examples 2 | # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help 3 | on: 4 | push: 5 | branches: [main, master] 6 | pull_request: 7 | branches: [main, master] 8 | release: 9 | types: [published] 10 | workflow_dispatch: 11 | 12 | name: pkgdown 13 | 14 | jobs: 15 | pkgdown: 16 | runs-on: ubuntu-latest 17 | # Only restrict concurrency for non-PR jobs 18 | concurrency: 19 | group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }} 20 | env: 21 | GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} 22 | steps: 23 | - uses: actions/checkout@v3 24 | 25 | - uses: r-lib/actions/setup-pandoc@v2 26 | 27 | - uses: r-lib/actions/setup-r@v2 28 | with: 29 | use-public-rspm: true 30 | 31 | - uses: r-lib/actions/setup-r-dependencies@v2 32 | with: 33 | extra-packages: any::pkgdown, local::. 34 | needs: website 35 | 36 | - name: Build site 37 | run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE) 38 | shell: Rscript {0} 39 | 40 | - name: Deploy to GitHub pages 🚀 41 | if: github.event_name != 'pull_request' 42 | uses: JamesIves/github-pages-deploy-action@v4.4.1 43 | with: 44 | clean: false 45 | branch: gh-pages 46 | folder: docs 47 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | 4 | 5 | /doc/ 6 | /Meta/ 7 | inst/doc 8 | -------------------------------------------------------------------------------- /.software-versions.txt: -------------------------------------------------------------------------------- 1 | The R project located at '~/Documents/RCode/medicaldata' was last run in the following software environment: 2 | 3 | 4 | R version 4.2.0 (2022-04-22) 5 | Platform: aarch64-apple-darwin20 (64-bit) 6 | Running under: macOS Monterey 12.5 7 | 8 | Matrix products: default 9 | BLAS: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib 10 | LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib 11 | 12 | locale: 13 | en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 14 | 15 | attached base packages: 16 | stats graphics grDevices utils datasets methods base 17 | 18 | other attached packages: 19 | fertile_1.1.9003 here_1.0.1 readxl_1.4.0 janitor_2.1.0 20 | medicaldata_0.2.0 flextable_0.7.2 forcats_0.5.1 stringr_1.4.0 21 | dplyr_1.0.9 purrr_0.3.4 readr_2.1.2 tidyr_1.2.0 22 | tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2 23 | -------------------------------------------------------------------------------- /CRAN-RELEASE: -------------------------------------------------------------------------------- 1 | This package was submitted to CRAN on 2021-08-12. 2 | Once it is accepted, delete this file and tag the release (commit f787f35). 3 | -------------------------------------------------------------------------------- /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: medicaldata 2 | Type: Package 3 | Title: Data Package for Medical Datasets 4 | Version: 0.2.0.9001 5 | Authors@R: c(person(given = "Peter", family = "Higgins", 6 | email="higgi13425@yahoo.com", role = c("aut", "cre"), 7 | comment = c(ORCID = "0000-0002-7111-0077"))) 8 | Date: 2022-04-21 9 | Description: Provides access to well-documented medical datasets for teaching. 10 | Featuring several from the Teaching of Statistics in the Health Sciences 11 | website , a few reconstructed datasets of historical significance in medical 12 | research, some reformatted and extended from existing R packages, 13 | and some data donations. 14 | Depends: 15 | R (>= 3.1) 16 | License: MIT + file LICENSE 17 | URL: https://higgi13425.github.io/medicaldata/, https://github.com/higgi13425/medicaldata/ 18 | BugReports: https://github.com/higgi13425/medicaldata/issues 19 | Suggests: 20 | flextable, 21 | janitor, 22 | knitr, 23 | markdown, 24 | rmarkdown, 25 | tidyverse 26 | VignetteBuilder: knitr 27 | Encoding: UTF-8 28 | LazyData: true 29 | Roxygen: list(markdown = TRUE) 30 | RoxygenNote: 7.2.3 31 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | YEAR: 2021 2 | COPYRIGHT HOLDER: medicaldata authors 3 | -------------------------------------------------------------------------------- /LICENSE.md: -------------------------------------------------------------------------------- 1 | # MIT License 2 | 3 | Copyright (c) 2021 medicaldata authors 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | -------------------------------------------------------------------------------- /NEWS.md: -------------------------------------------------------------------------------- 1 | # medicaldata (development version) 2 | 3 | # medicaldata 0.2.0.9000 4 | * dev version, adding examples in vignettes 5 | * hoping to add new datasets (donate if you can!) 6 | - added cath from Frank Harrell 7 | - added abm from Frank Harrell 8 | 9 | # medicaldata 0.2.0 10 | * stable version 11 | * all datasets now have complete documentation for help() 12 | * packagedown site built 13 | * updated to 0.2.0, on CRAN as of 2021-08-16. 14 | 15 | # medicaldata 0.1.0.9000 16 | * Added a `NEWS.md` file to track changes to the package. 17 | -------------------------------------------------------------------------------- /R/Indometh.R: -------------------------------------------------------------------------------- 1 | #' @title 2 | #' Cohort Study of the Pharmacokinetics of Intravenous Indomethacin 3 | #' 4 | #' @description 5 | #' Results of a Cohort Study of the Pharmacokinetics of Intravenous Indomethacin, with plasma concentrations over time (**more details** available below the variable definitions). \cr 6 | #' @details 7 | #' This data set contains data on 6 healthy volunteer subjects who participated in a pharmacokinetic study of intravenous indomethacin. Indomethacin is an anti-inflammatory and pain-relieving non-steroidal medication. It can be administered by the intravenous, oral, or rectal suppository routes. Some of the indomethacin is excreted in the bile and reabsorbed by the intestine. This phenomenon, called enterohepatic circulation, keeps the drug around longer than would be expected otherwise. \cr 8 | #' Each subject in Study 1 (intravenous route) received a single 50 mg dose of radioactively labeled indomethacin (^14^-carbon-labeled, with each dose containing 25 microCuries of radioactivity). Subjects received a standard meal (one 8-oz can of Metrecal, 8 oz of whole milk, and one medium-size apple) 30 rain prior to medication and 8 oz of water every 2 hr throughout the waking hours to ensure adequate urine output.\cr 9 | #' Blood samples were taken at frequent intervals over the first 8 hours after dosing, and the quantity of indomethacin in the plasma (as well as stool and urine) at each time point was measured in micrograms per milliliter. This data set only contains the plasma measurements from Table 1 on page 258 of the manuscript. While this paper was published in 1976 (post-Tuskegee reveal), there is no mention of ethics review, IRB review, or consent of the healthy volunteers. \cr 10 | #' The abstract from the original manuscript: \cr 11 | #' There are no discernible quantitative differences in the biotransformation and the excretion of indomethacin following oral, rectal, and intravenous administration of indomethacin-2-^14^C. Approximately 50% (range 24-115% for n = 6) of an intravenous dose undergoes enterohepatic circulation. Thus the bioavailability of indomethacin to the systemic circulation may exceed the administered dose. Relative to the intravenous dose, indomethacin is 80 and 100% bioavailable from suppositories and capsules, respectively. Absorption and/or reabsorption appears to be more rapid and uniform by the rectal route. Recognition of the attributes of biliary recycling also helps to explain the observed variability in apparent plasma half-life, while their neglect requires alternative explanations for anomalies between the disappearance rate from plasma and the corresponding appearance rate in urine.\cr 12 | #' @format A data frame with 66 observations and 3 variables 13 | #' \describe{ 14 | #' \item{Subject}{subject id number for each participant; type: character} 15 | #' \item{time}{Time from initial dose in hours; type: double} 16 | #' \item{conc}{Concentration of indomethacin in the plasma in micrograms per milliliter' type: double} 17 | #' } 18 | #' 19 | #' @source Kwan, Breault, Umbenhauer, McMahon and Duggan (1976) Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Pharmacokinetics and Biopharmaceutics. 1976 Jun;4(3):255-80. doi: 10.1007/BF01063617. 20 | #' 21 | "indometh" 22 | -------------------------------------------------------------------------------- /R/cath.R: -------------------------------------------------------------------------------- 1 | #' @title 2 | #' Observational Dataset of Cardiac Catheterization 3 | #' 4 | #' @description 5 | #' This dataset is from the Duke University Cardiovascular Disease Databank, as provided by Frank Harrell at https://hbiostat.org. The patients were referred to Duke University Medical Center for chest pain. Cardiac catheterization (also known as a 'cath') is performed to diagnose and open blockages in these arteries, often followed by stenting to keep them open.\cr 6 | #' When I (PDRH) was an intern at Duke in 2001, it was a nightly occurrence for the 'cath bus' to arrive from Lumberton, North Carolina with 8 patients in chest pain (all at once), lighting up the pager of the cardiology intern with multiple requests for nitroglycerin. \cr 7 | #' @details 8 | #' Coronary artery disease, or atherosclerosis of the blood vessels supplying the heart, is the most common cause of death in the United States. Duke University Medical Center is located in the southeastern US, in a region of highly prevalent coronary artery disease (CAD), associated with frequent smoking, diabetes, and obesity, along with genetic risk factors for early-onset CAD in the local Lumbee Indian tribe. \cr\cr 9 | #' At catheterization, a reduction in artery diameter by at least 75% is considered a significant reduction in blood flow that puts downstream heart muscle at risk of ischemia. Significant coronary disease can be treated with multiple medications, or by opening the partially blocked artery. This is done with a via a catheter through blood vessels in the wrist or the groin, and threaded through blood vessels to the heart. Once at the location of the blockage, the narrowed area can be opened with a balloon, and the newly opened artery kept open with a carefully placed coronary stent. \cr\cr 10 | #' The **sigdz** variable is an indicator for the presence of a blockage of at least 75% in any one of the left main coronary artery or in any of the three distributing arteries - the LAD, LCA, and RCA.\cr\cr 11 | #' The **tvdlm** variable is an indicator for one of two results from the catheterization. One is three vessel disease - having blockages of all three of the left anterior descendig (LAD) coronary artery, the left circumflex coronary artery (LCA), and the right coronary artery (RCA). This occurs most commonly in association with diabetes, and with longstanding severe CAD. The other is left main disease, which is blockage of the artery that feeds both the LAD and the LCA. Blockage in the left main coronary artery is frequently fatal, and is colloquially known as a "widowmaker". \cr\cr 12 | #' Some interesting potential analyses include predicting the probability of significant (>= 75% diameter narrowing in at least one major coronary artery) coronary disease, and predicting the probability of severe coronary disease given that some significant disease is present. The first analysis would use sigdz as a response variable, and the second would use tvdlm on the subset of patients having sigdz=1. \cr 13 | #' \cr 14 | #' @format A data frame with 3504 observations and 6 variables 15 | #' \describe{ 16 | #' \item{sex}{gender, coded as 0=male and 1=female; type: double} 17 | #' \item{age}{age in years; type: double} 18 | #' \item{cad_dur}{duration of symptoms of chest pain, in days; type: double} 19 | #' \item{choleste}{serum cholesterol level in milligrams per deciliter. Note that 35.56% of observations for cholesterol are missing; type: double} 20 | #' \item{sigdz}{significant coronary artery disease found on cardiac catheterization: 0=no, 1=yes; type: double} 21 | #' \item{tvdlm}{three vessel or left main coronary artery disease found on cardiac catheterization. There are 3 missing observations for tvdlm: 0=no, 1=yes; type: double} 22 | #' } 23 | #' 24 | #' @source This data set is provided generously by Frank Harrell, from his website \href{https://hbiostat.org/data/}{here}, as the dataset "acath". 25 | #' 26 | "cath" 27 | -------------------------------------------------------------------------------- /R/cdystonia.R: -------------------------------------------------------------------------------- 1 | #' @title 2 | #' RCT of Botulinum Toxin B for Cervical Dystonia 3 | #' 4 | #' @description 5 | #' Longitudinal results of a randomized, placebo-controlled trial of botulinum toxin type B (BotB) in 109 subjects across 9 sites. (**more details** available below the variable definitions). \cr 6 | #' 7 | #' @details 8 | #' Cervical dystonia, also called spasmodic torticollis, is a painful condition in which your neck muscles contract involuntarily, causing your head to twist or turn to one side. Cervical dystonia can also cause your head to uncontrollably tilt forward or backward. \cr 9 | #' A rare disorder that can occur at any age, cervical dystonia most often occurs in middle-aged people, women more than men. Symptoms generally begin gradually and then reach a point where they don't get substantially worse.\cr 10 | #' 109 subjects across 9 sites were randomized to placebo (N = 36), 5000 units of botulinum toxin B (N = 36), or 10,000 units of botulinum toxin B (N = 37), injected once at baseline into the affected muscle to partially paralyze it and make it relax, releasing the spasmed side of the neck and head.\cr 11 | #' The response variable is the score on the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS-Total on a 0-87 scale), which measures the severity, pain, and disability of cervical dystonia (higher scores mean more impairment) at weeks 0 (baseline), 2, 4, 8, 12, and 16. It is expected that the single botox injection at week 0 may wear off over time. 12 | #' 13 | #' 14 | #' @format A data frame with 631 observations and 7 variables 15 | #' \describe{ 16 | #' \item{week}{week of measurement; type: numeric} 17 | #' \item{site}{study site, levels: 1-9; type: double} 18 | #' \item{id}{patient id at each site (not unique across sites); type: numeric 1-19} 19 | #' \item{treat}{treatment assignment: levels: 1 = placebo, 2 = botox B 5000 units, 3 = botox B 10,000 units; type: double 1-3)} 20 | #' \item{age}{age in years, range 26-83; type: numeric} 21 | #' \item{sex}{sex of participant, 1 = F, 2 = M; type: double} 22 | #' \item{twstrs}{total score of Toronto Western Spasmodic Torticollis Rating Scale, range 0-87; type: numeric} 23 | #' } 24 | #' 25 | #' @source This data set is from a study published in 1999 in the journal *Neurology*,\cr 26 | #' \cr 27 | #' A. Brashear, M.F. Lew, D.D. Dykstra, C.L. Comella, S.A. Factor, R.L. Rodnitzky, R. Trosch, C. Singer, M.F. Brin, J.J. Murray, J.D. Wallace, A. Willmer–Hulme, M. Koller (1999), *Safety and efficacy of NeuroBloc (botulinum toxin type B) in type A–responsive cervical dystonia*. Neurology, 53(7), 1439. 28 | #' 29 | #' This dataset has been passed through Statistical Methods for the Analysis of Repeated Measurements by Charles S. Davis, pp. 161-163 (Springer, 2002) and can also be found at Frank Harrell's website [here](https://hbiostat.org/data/). 30 | #' 31 | "cdystonia" 32 | -------------------------------------------------------------------------------- /R/covid_testing.R: -------------------------------------------------------------------------------- 1 | #' @title 2 | #' Deidentified Results of COVID-19 testing at the Children's Hospital of Pennsylvania (CHOP) in 2020 3 | #' 4 | #' @description 5 | #' A dataset containing details of SARS-CoV-2 testing in 2020 at CHOP 6 | #' 7 | #' @details 8 | #' Data on testing for SARS-CoV2 from days 4-107 of the COVID pandemic in 2020. CHOP is a pediatric hospital in Philadelphia, Pennsylvania, USA. These data have been anonymized, time-shifted, and permuted. 9 | #' 10 | #' @format A data frame with 15524 observations and 17 variables 11 | #' \describe{ 12 | #' \item{subject_id}{id number for each subject; type: numeric} 13 | #' \item{fake_first_name}{an auto-generated fake first name; type: character} 14 | #' \item{fake_last_name}{an auto-generated fake last name; character} 15 | #' \item{gender}{anonymized Gender, levels: female, male; type: character} 16 | #' \item{pan_day}{day after start of pandemic; type: numeric} 17 | #' \item{test_id}{test that was performed, levels: covid, xcvd1; type: character} 18 | #' \item{clinic_name}{Clinic or ward where the specimen was collected, 88 levels; type: character} 19 | #' \item{result}{result of test, levels: positive, negative, invalid; type: character} 20 | #' \item{demo_group}{patient group, levels: patient, misc_adult, client, other adult, unidentified; type: character} 21 | #' \item{age}{Age of subject at time of specimen collection (Anonymized), units = years; type: numeric} 22 | #' \item{drive_thru_ind}{Whether the specimen was collected via a drive-thru site, levels: 1: Collected at drive-thru site; 0: Not collected at drive-thru site; type: numeric} 23 | #' \item{ct_result}{Cycle at which threshold reached during PCR, range: 14.05-45; type: numeric} 24 | #' \item{orderset}{Whether an order set was used for test order, levels: 1: Collected via orderset; 0: Not collected via orderset; numeric} 25 | #' \item{payor_group}{Payor associated with order, levels: commercial, government, unassigned, medical assistance, self pay, charity care, other; type: character} 26 | #' \item{patient_class}{Disposition of subject at time of collection, levels: inpatient, emergency, observation, recurring outpatient, outpatient, not applicable, day surgery, admit after surgery-obs, admit after surgery-ip; type: character} 27 | #' \item{col_rec_tat}{Time elapsed between collect time and receive time, range: 0 - 61370.2, units = hours; type: numeric} 28 | #' \item{rec_ver_tat}{Time elapsed between receive time and verification time, range: -18.6 - 218.2, units = hours; type: numeric} 29 | #' ... 30 | #' } 31 | #' @source This data set is from Amrom E. Obstfeld, who de-identified data on COVID-19 testing during 2020 at CHOP (Children's Hospital of Pennsylvania). This data set contains data concerning testing for SARS-CoV2 via PCR as well as associated metadata. These data have been anonymized, time-shifted, and permuted. 32 | #' 33 | "covid_testing" 34 | -------------------------------------------------------------------------------- /R/esoph_ca.R: -------------------------------------------------------------------------------- 1 | #' esoph_ca: Esophageal Cancer dataset 2 | #' 3 | #' @description 4 | #' Data from a case-control study of esophageal cancer in Ille-et-Vilaine, France, evaluating the effects of smoking and alcohol on the incidence of esophageal cancer. Smoking and alcohol are associated risk factors for squamous cell cancer of the esophagus, rather than adenocarcinoma of the esophagus, which is associated with obesity and esophageal reflux (more details available below the variable definitions). 5 | #' 6 | #' @details 7 | #' An original base R dataset, though of somewhat unclear origin. The statistical textbook source is clear, though it is not clear which of the original epidemiological papers on esophageal cancer in Ille-et-Vilaine is referred to by this dataset. The original authors of the medical study were **not** credited in the base R dataset. There are several possible papers in PubMed, none of which quite match up with this dataset. This could be from Tuyns, AJ, et al., Bull Cancer, 1977;64(1):45-60, but this paper reports 778 controls, rather than the 975 found here. A 1975 paper from the same group reported 718 cases (Int J Epidemiol, 1975 Mar;4(1):55-9. doi: 10.1093/ije/4.1.55.). There is also another possible source - a 1975 paper from the same group, *Usefulness of population controls in retrospective studies of alcohol consumption. Experience from a case--control study of esophageal cancer in Ille-et-Vilaine, France*, Journal of Studies on Alcohol, 39(1): 175-182 (1978), which is behind a publisher paywall. 8 | #' 9 | #' @format A data frame with 88 rows and 5 variables, with 200 cases and 975 controls. 10 | #' 11 | #' \describe{ 12 | #' \item{agegp}{6 levels of age: "25-34", "35-44", "45-54", "55-64", "65-74", "75+"; type: ordinal factor} 13 | #' \item{alcgp}{4 levels of alcohol consumption: "0-39g/day", "40-79", "80-119", "120+"; type: ordinal factor} 14 | #' \item{tobgp}{4 levels of tobacco consumption: "0-9g/day", "10-19", '20-29", "30+"; type: ordinal factor} 15 | #' \item{ncases}{Number of cases; type: integer} 16 | #' \item{ncontrols}{Number of controls; type: integer} 17 | #' } 18 | #' 19 | #'@section Figure 1 Benign FNA of Breast: Benign fine needle aspirate (FNA) of a breast lesion. Notice the regular size of cells and nuclei, which are organized in orderly spacing. The nuclei are homogeneously dark with few visible nucleoli. 20 | #'\if{html}{\figure{benign_breast.png}{options: width=100\%}} 21 | #' 22 | #'@section Figure 2 Cancerous FNA of Breast: Malignant (cancerous) fine needle aspirate (FNA) of a breast lesion. Notice the very irregular size of cells and nuclei, which are are disorganized and seem to be growing over each other. The nuclei are also less homogeneously dark and more granular, suggesting active transcription from the dark nucleoli within each nucleus. 23 | #'\if{html}{\figure{malignant_breast.png}{options: width=100\%}} 24 | #' 25 | #' @source Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. IARC Lyon / Oxford University Press. 26 | #' Originally in base R datasets. 27 | "esoph_ca" 28 | 29 | -------------------------------------------------------------------------------- /R/polyps.R: -------------------------------------------------------------------------------- 1 | #' @title 2 | #' RCT of Sulindac for Polyp Prevention in Familial Adenomatous Polyposis 3 | #' 4 | #' @description 5 | #' Results of a randomized, placebo-controlled trial of sulindac in the reduction of colonic polyps in Familial Adenomatous Polyposis (FAP) (**more details** available below the variable definitions). \cr 6 | #' @details 7 | #' FAP is an inherited condition caused by mutations in the APC (Adenomatous Polyposis Coli) gene that leads to early and frequent formation of precancerous polyps of the colon at a young age, and invariably leads to the development of colon cancer at a young age. \cr 8 | #' Early, frequent surveillance colonoscopy and polyp removal is helpful, but this study examined whether there is a beneficial effect of preventive medical therapy with the nonsteroidal pain reliever, sulindac, versus placebo in a RCT vs placebo in 22 participants, with polyp number measured (via colonoscopy) at baseline, 3 months, and 12 months after starting the study drug. Note that one subject did not return for the 12 month colonoscopy. 9 | #' 10 | #' 11 | #' @format A data frame with 22 observations and 7 variables 12 | #' \describe{ 13 | #' \item{participant_id}{id number for each participant; type: character} 14 | #' \item{sex}{participant sex, levels: female, male; type: factor} 15 | #' \item{age}{age in years; type: numeric} 16 | #' \item{baseline}{number of colonic polyps at baseline; type: numeric} 17 | #' \item{treatment}{treatment assignment, levels: sulindac, placebo; type: factor} 18 | #' \item{number3m}{number of colonic polyps at 3 months; type: numeric} 19 | #' \item{number12m}{number of colonic polyps at 12 months; type: numeric} 20 | #' } 21 | #' 22 | #' @source This data set is from a study published in 1993 in the New England Journal of Medicine, 23 | #' 24 | #' F. M. Giardiello, S. R. Hamilton, A. J. Krush, S. Piantadosi, L. M. Hylind, P. Celano, S. V. Booker, C. R. Robinson and G. J. A. Offerhaus (1993), Treatment of colonic and rectal adenomas with sulindac in familial adenomatous polyposis. New England Journal of Medicine, 328(18), 1313-1316. 25 | #' 26 | #' This dataset is derived from and improved upon from the {HSAUR} package. 27 | #' 28 | "polyps" 29 | -------------------------------------------------------------------------------- /R/theoph.R: -------------------------------------------------------------------------------- 1 | #' @title 2 | #' Cohort Study of the Pharmacokinetics of Oral Theophylline 3 | #' 4 | #' @description 5 | #' Results of a Cohort Study of the Pharmacokinetics of Oral Theophylline, with plasma concentrations over time (**more details** available below the variable definitions). \cr 6 | #' @details 7 | #' This data set is from a pharmacokinetic study of oral dosing of the anti-asthma medication, theophylline, in 12 subjects over 25 hours, published By Dr. Robert A. Upton around 1980. The original publication, if any, is unclear and not cited. These data were used in a package named `nlme`, and reported in Boeckmann, A.J., et al.Dr. Upton did publish several papers on theophylline pharmacokinetics around 1980-1984, and these data could have been from one of these.\cr 8 | #' Theophylline is an methylxanthine anti-asthma medication, which acts as a bronchodilator, with secondary effects to strengthen diaphragm contraction, reduce pulmonary artery pressures, and reduce mast cell release. It can be administered by the intravenous, oral, or rectal suppository routes.\cr 9 | #' Each subject in this Study (oral route) received a single oral dose of theophylline. 10 | #' Blood samples were taken at frequent intervals over the first 25 hours after dosing, and the quantity of theophylline in the plasma at each time point was measured in micrograms per milliliter. \cr 11 | #' Unfortunately, the theophylline plasma level in blood varies considerably between patients, because of differences in drug clearance, which is affected by body mass, age, smoking, liver and heart function, and viral infections. To complicate this drug further, it has important interactions with a number of other common medicines which can increase or decrease the drug level. Each subject in this study received a single oral dose of 300 mg of theophylline, which has been converted to a milligrams per kilogram dose. Blood samples were taken at frequent intervals over the next 25 hours after dosing, and the quantity of theophylline in the plasma at each time point was measured in micrograms per milliliter of plasma. \cr 12 | #' @format A data frame with 132 observations and 5 variables 13 | #' \describe{ 14 | #' \item{Subject}{subject id number for each participant; type: ordinal factor} 15 | #' \item{Wt}{Weight in kilograms; type: double} 16 | #' \item{Dose}{Dose in milligrams per kilogram; type: double} 17 | #' \item{Time}{Time from initial dose in hours; type: double} 18 | #' \item{conc}{Concentration of theophylline in the plasma in micrograms per milliliter' type: double} 19 | #' } 20 | #' 21 | #' @source Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994), NONMEM Users Guide: Part V, NONMEM Project Group, University of California, San Francisco. 22 | #' Note that the original data collector, Robert A. Upton, is not credited, nor is the original work cited. 23 | #' 24 | #' 25 | "theoph" 26 | -------------------------------------------------------------------------------- /_pkgdown.yml: -------------------------------------------------------------------------------- 1 | home: 2 | title: An R Package Of Medical Data For Teaching 3 | description: Medical Datasets for Teaching R 4 | 5 | template: 6 | bootstrap: 5 7 | bootswatch: lumen 8 | 9 | url: https://higgi13425.github.io/medicaldata/ 10 | 11 | reference: 12 | - title: Datasets 13 | - contents: 14 | - scurvy 15 | - strep_tb 16 | - polyps 17 | - cdystonia 18 | - indo_rct 19 | - licorice_gargle 20 | - laryngoscope 21 | - opt 22 | - abm 23 | - cath 24 | - covid_testing 25 | - esoph_ca 26 | - supraclavicular 27 | - blood_storage 28 | - cytomegalovirus 29 | - smartpill 30 | - indometh 31 | - theoph 32 | - diabetes 33 | - thiomon 34 | -------------------------------------------------------------------------------- /cran-comments.md: -------------------------------------------------------------------------------- 1 | ## Test environments 2 | * local OS X install, R 4.0.4 3 | * Linux via rhub, using debian-gcc-devel 4 | * win-builder (devel and release and old) 5 | 6 | ## R CMD check results 7 | There were no ERRORs or WARNINGs. 8 | 9 | One NOTE: 10 | * checking CRAN incoming feasibility ... NOTE 11 | Maintainer: 'Peter Higgins ' 12 | 13 | - Yes, I am the maintainer. 14 | * This is a first submission of a new package. 15 | 16 | - the link to the TSHS datasets website was added to the Description: field of the DESCRIPTION file at the suggestion of Gregor Seyer. 17 | 18 | ## Downstream dependencies 19 | None. 20 | This is a new package, and a data-only package, so it seems unlikely to develop a lot of downstream dependencies. 21 | 22 | ## Permissions 23 | The datasets from Teaching of Statistics in the Health Sciences have 24 | Creative Commons ShareAlike licenses, but I also obtained specific permission to 25 | package these datasets (and others) into the {medicaldata} package from Carol Bigelow, the founder and administrator of the TSHS Resources Portal. 26 | 27 | The scurvy and strep_tb datasets were published over 250 and over 70 years ago, respectively. All of the authors have passed on. 28 | 29 | The covid_testing dataset was generously donated by Amrom E. Obstfeld. 30 | 31 | The indo_rct dataset was generously donated by B. Joseph Elmunzer. 32 | 33 | The indometh, theoph, and esoph_ca datasets are available in base R, though poorly documented. I have added substantially to the documentation to make these more useful for teaching. 34 | 35 | The polyps dataset is available in the HSAUR package, though it is divided into separate datasets (polyps, polyps3), without making clear which observations correspond, and which particpants had missing data. This is improved, with a single united dataset, with substantially more documentation of the randomized controlled trial, in this package. 36 | -------------------------------------------------------------------------------- /data-raw/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/data-raw/.DS_Store -------------------------------------------------------------------------------- /data-raw/.Rapp.history: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/data-raw/.Rapp.history -------------------------------------------------------------------------------- /data-raw/Cytomegalovirus.Rdata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/data-raw/Cytomegalovirus.Rdata -------------------------------------------------------------------------------- /data-raw/IndoRCT(subclasses1).dta: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/data-raw/IndoRCT(subclasses1).dta -------------------------------------------------------------------------------- /data-raw/Laryngoscope.Rdata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/data-raw/Laryngoscope.Rdata -------------------------------------------------------------------------------- /data-raw/OPT_Study_PersonLevel_Data.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/data-raw/OPT_Study_PersonLevel_Data.RData -------------------------------------------------------------------------------- /data-raw/abm.Rdata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/data-raw/abm.Rdata -------------------------------------------------------------------------------- /data-raw/abm.dta: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/data-raw/abm.dta -------------------------------------------------------------------------------- /data-raw/blood_storage.Rdata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/data-raw/blood_storage.Rdata -------------------------------------------------------------------------------- /data-raw/cath.Rdata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/data-raw/cath.Rdata -------------------------------------------------------------------------------- /data-raw/covid_testing.Rdata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/data-raw/covid_testing.Rdata -------------------------------------------------------------------------------- /data-raw/covid_testing_code_book.csv: -------------------------------------------------------------------------------- 1 | variable,position,Variable Label,units,Codes 2 | subject_id,1,Subject ID,NA,NA 3 | fake_first_name,2,Pseudo First Name,NA,NA 4 | fake_last_name,3,Pseudo Last Name,NA,NA 5 | gender,4,Gender (Anonymized),NA,NA 6 | pan_day,5,Day after start of pandemic in which specimen was collected (Anonymized),Day,NA 7 | test_id,6,Test that was performed,NA,NA 8 | clinic_name,7,Clinic or ward where the specimen was collected,NA,NA 9 | result,8,Result of the test,NA,NA 10 | demo_group,9,Type of subject,NA,NA 11 | age,10,Age of subject at time of specimen collection (Anonymized),Years,NA 12 | drive_thru_ind,11,Whether the specimen was collected via a drive-thru site,NA,1: Collected at drive-thru site; 0: Not collected at drive-thru site 13 | ct_result,12,Cycle at which threshold reached during PCR,NA,NA 14 | orderset,13,Whether and order set was used for test order,NA,1: Collected via orderset; 0: Not collected via orderset 15 | payor_group,14,Payor associate with order,NA,NA 16 | patient_class,15,Disposition of subject at time of collection,NA,NA 17 | col_rec_tat,16,Time elapsed between collect time and receive time,Hours,NA 18 | rec_ver_tat,17,Time elapsed between receive time and verification time,Hours,NA 19 | "This data set contains data concerning testing for SARS-CoV2 via PCR as well as associated meta data. The data is anonymized, time-shifted, and permuted.",NA,NA,NA,NA 20 | -------------------------------------------------------------------------------- /data-raw/diabetes.Rdata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/data-raw/diabetes.Rdata -------------------------------------------------------------------------------- /data-raw/esoph.Rdata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/data-raw/esoph.Rdata -------------------------------------------------------------------------------- /data-raw/esoph_ca.Rdata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/data-raw/esoph_ca.Rdata -------------------------------------------------------------------------------- /data-raw/indo_rct.Rdata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/data-raw/indo_rct.Rdata -------------------------------------------------------------------------------- /data-raw/indo_rct_pep.dta: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/data-raw/indo_rct_pep.dta -------------------------------------------------------------------------------- /data-raw/indometh.Rdata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/data-raw/indometh.Rdata -------------------------------------------------------------------------------- /data-raw/join_data/birthdays.csv: -------------------------------------------------------------------------------- 1 | "","name","dob" 2 | "1","Rudolf Virchow","10/13/1821" 3 | "2","Virginia Apgar","6/7/1909" 4 | "3","William Osler","7/12/1849" 5 | -------------------------------------------------------------------------------- /data-raw/join_data/demog.csv: -------------------------------------------------------------------------------- 1 | "","mrn","first_name","last_name","dob","census_tract","visit_date","visit_id" 2 | "1","012345678","Anthony","Stark","5/29/1970","06037702002","4/26/2019","123456" 3 | "2","000001234","Steven","Rogers","7/4/1918","36047061600","4/27/2019","133123" 4 | "3","134567890","Natasha","Romanoff","12/3/1984","36061015002","4/27/2019","133456" 5 | "4","134567914","Wanda","Maximoff","2/10/1989","36061016900","4/27/2019","133412" 6 | -------------------------------------------------------------------------------- /data-raw/join_data/hometowns.csv: -------------------------------------------------------------------------------- 1 | "","name","hometown" 2 | "1","Rudolf Virchow","Berlin" 3 | "2","Virginia Apgar","Westfield, NJ" 4 | "3","Hippocrates","Kos" 5 | -------------------------------------------------------------------------------- /data-raw/join_data/hometowns2.csv: -------------------------------------------------------------------------------- 1 | "","Name","hometown" 2 | "1","Rudolf Virchow","Berlin" 3 | "2","Virginia Apgar","Westfield, NJ" 4 | "3","Hippocrates","Kos" 5 | -------------------------------------------------------------------------------- /data-raw/join_data/labs.csv: -------------------------------------------------------------------------------- 1 | "","visit_id","wbc_e9_L","hgb_g_dL","hct_pct","plt_k_uL" 2 | "1","123456",18.2,8.7,25.3,188 3 | "2","133123",14.3,9.4,28.1,226 4 | "3","133456",19.6,7.3,22.4,147 5 | "4","133412",10.9,11.1,33.5,284 6 | -------------------------------------------------------------------------------- /data-raw/join_data/sdoh.csv: -------------------------------------------------------------------------------- 1 | "","census_tract","svi_theme1","svi_theme2","svi_theme3","svi_theme4","svi_overall" 2 | "1","06037702002",0.0081,0.112,0.3014,0.0701,0.0288 3 | "2","36047061600",0.2345,0.172,0.3834,0.2911,0.3315 4 | "3","36061015002",0.0183,0.101,0.3321,0.0634,0.0302 5 | "4","36061016900",0.0103,0.061,0.1321,0.0424,0.0282 6 | -------------------------------------------------------------------------------- /data-raw/join_data/vitals.csv: -------------------------------------------------------------------------------- 1 | "","visit_id","sbp_mm_hg","dbp_mm_hg","hr_bpm","rr_bpm" 2 | "1","123456",104,66,112,24 3 | "2","133123",118,72,92,17 4 | "3","133456",84,51,114,28 5 | "4","133412",92,60,110,22 6 | 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7 | "6","006","female",13,35,"placebo",31,61 8 | "7","007","female",23,11,"sulindac",6,1 9 | "8","008","male",34,12,"placebo",20,7 10 | "9","009","male",50,7,"placebo",7,15 11 | "10","010","male",19,318,"placebo",347,44 12 | "11","011","male",17,160,"sulindac",142,25 13 | "12","012","female",23,8,"sulindac",1,3 14 | "13","013","male",22,20,"placebo",16,28 15 | "14","014","male",30,11,"placebo",20,10 16 | "15","015","male",27,24,"placebo",26,40 17 | "16","016","male",23,34,"sulindac",27,33 18 | "17","017","female",22,54,"placebo",45,46 19 | "18","018","male",13,16,"sulindac",10,NA 20 | "19","019","male",34,30,"placebo",30,50 21 | "20","020","female",23,10,"sulindac",6,3 22 | "21","021","female",22,20,"sulindac",5,1 23 | "22","022","male",42,12,"sulindac",8,4 24 | -------------------------------------------------------------------------------- /data-raw/scurvy.Rdata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/data-raw/scurvy.Rdata -------------------------------------------------------------------------------- /data-raw/scurvy.csv: -------------------------------------------------------------------------------- 1 | "","study_id","treatment","dosing_regimen_for_scurvy","gum_rot_d6","skin_sores_d6","weakness_of_the_knees_d6","lassitude_d6","fit_for_duty_d6" 2 | "1","001","cider","1 quart per day","2_moderate","2_moderate","2_moderate","2_moderate","0_no" 3 | "2","002","cider","1 quart per day","2_moderate","1_mild","2_moderate","3_severe","0_no" 4 | "3","003","dilute_sulfuric_acid","25 drops of elixir of vitriol, three times a day","1_mild","3_severe","3_severe","3_severe","0_no" 5 | "4","004","dilute_sulfuric_acid","25 drops of elixir of vitriol, three times a day","2_moderate","3_severe","3_severe","3_severe","0_no" 6 | "5","005","vinegar","two spoonfuls, three times daily","3_severe","3_severe","3_severe","3_severe","0_no" 7 | "6","006","vinegar","two spoonfuls, three times daily","3_severe","3_severe","3_severe","3_severe","0_no" 8 | "7","007","sea_water","half pint daily","3_severe","3_severe","3_severe","3_severe","0_no" 9 | "8","008","sea_water","half pint daily","3_severe","3_severe","3_severe","3_severe","0_no" 10 | "9","009","citrus","two lemons and an orange daily","1_mild","1_mild","0_none","1_mild","0_no" 11 | "10","010","citrus","two lemons and an orange daily","0_none","0_none","0_none","0_none","1_yes" 12 | "11","011","purgative_mixture","a nutmeg-sized paste of garlic, mustard seed, horseradish, balsam of Peru, and gum myrrh three times a day","3_severe","3_severe","3_severe","3_severe","0_no" 13 | "12","012","purgative_mixture","a nutmeg-sized paste of garlic, mustard seed, horseradish, balsam of Peru, and gum myrrh three times a day","3_severe","3_severe","3_severe","3_severe","0_no" 14 | 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We remove the former (to 2 | // be compatible with the behavior of Pandoc < 2.8). 3 | document.addEventListener('DOMContentLoaded', function(e) { 4 | var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); 5 | var i, h, a; 6 | for (i = 0; i < hs.length; i++) { 7 | h = hs[i]; 8 | if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 9 | a = h.attributes; 10 | while (a.length > 0) h.removeAttribute(a[0].name); 11 | } 12 | }); 13 | -------------------------------------------------------------------------------- /docs/articles/Introduction to the package_files/header-attrs-2.9/header-attrs.js: -------------------------------------------------------------------------------- 1 | // Pandoc 2.9 adds attributes on both header and div. We remove the former (to 2 | // be compatible with the behavior of Pandoc < 2.8). 3 | document.addEventListener('DOMContentLoaded', function(e) { 4 | var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); 5 | var i, h, a; 6 | for (i = 0; i < hs.length; i++) { 7 | h = hs[i]; 8 | if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 9 | a = h.attributes; 10 | while (a.length > 0) h.removeAttribute(a[0].name); 11 | } 12 | }); 13 | -------------------------------------------------------------------------------- /docs/articles/Introduction_files/header-attrs-2.10/header-attrs.js: -------------------------------------------------------------------------------- 1 | // Pandoc 2.9 adds attributes on both header and div. We remove the former (to 2 | // be compatible with the behavior of Pandoc < 2.8). 3 | document.addEventListener('DOMContentLoaded', function(e) { 4 | var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); 5 | var i, h, a; 6 | for (i = 0; i < hs.length; i++) { 7 | h = hs[i]; 8 | if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 9 | a = h.attributes; 10 | while (a.length > 0) h.removeAttribute(a[0].name); 11 | } 12 | }); 13 | -------------------------------------------------------------------------------- /docs/articles/Introduction_files/header-attrs-2.11.3/header-attrs.js: -------------------------------------------------------------------------------- 1 | // Pandoc 2.9 adds attributes on both header and div. We remove the former (to 2 | // be compatible with the behavior of Pandoc < 2.8). 3 | document.addEventListener('DOMContentLoaded', function(e) { 4 | var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); 5 | var i, h, a; 6 | for (i = 0; i < hs.length; i++) { 7 | h = hs[i]; 8 | if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 9 | a = h.attributes; 10 | while (a.length > 0) h.removeAttribute(a[0].name); 11 | } 12 | }); 13 | -------------------------------------------------------------------------------- /docs/articles/Introduction_files/header-attrs-2.11/header-attrs.js: -------------------------------------------------------------------------------- 1 | // Pandoc 2.9 adds attributes on both header and div. We remove the former (to 2 | // be compatible with the behavior of Pandoc < 2.8). 3 | document.addEventListener('DOMContentLoaded', function(e) { 4 | var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); 5 | var i, h, a; 6 | for (i = 0; i < hs.length; i++) { 7 | h = hs[i]; 8 | if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 9 | a = h.attributes; 10 | while (a.length > 0) h.removeAttribute(a[0].name); 11 | } 12 | }); 13 | -------------------------------------------------------------------------------- /docs/articles/Introduction_files/header-attrs-2.8/header-attrs.js: -------------------------------------------------------------------------------- 1 | // Pandoc 2.9 adds attributes on both header and div. We remove the former (to 2 | // be compatible with the behavior of Pandoc < 2.8). 3 | document.addEventListener('DOMContentLoaded', function(e) { 4 | var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); 5 | var i, h, a; 6 | for (i = 0; i < hs.length; i++) { 7 | h = hs[i]; 8 | if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 9 | a = h.attributes; 10 | while (a.length > 0) h.removeAttribute(a[0].name); 11 | } 12 | }); 13 | -------------------------------------------------------------------------------- /docs/articles/Introduction_files/header-attrs-2.9/header-attrs.js: -------------------------------------------------------------------------------- 1 | // Pandoc 2.9 adds attributes on both header and div. We remove the former (to 2 | // be compatible with the behavior of Pandoc < 2.8). 3 | document.addEventListener('DOMContentLoaded', function(e) { 4 | var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); 5 | var i, h, a; 6 | for (i = 0; i < hs.length; i++) { 7 | h = hs[i]; 8 | if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 9 | a = h.attributes; 10 | while (a.length > 0) h.removeAttribute(a[0].name); 11 | } 12 | }); 13 | -------------------------------------------------------------------------------- /docs/articles/making_tables_files/header-attrs-2.10/header-attrs.js: -------------------------------------------------------------------------------- 1 | // Pandoc 2.9 adds attributes on both header and div. We remove the former (to 2 | // be compatible with the behavior of Pandoc < 2.8). 3 | document.addEventListener('DOMContentLoaded', function(e) { 4 | var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); 5 | var i, h, a; 6 | for (i = 0; i < hs.length; i++) { 7 | h = hs[i]; 8 | if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 9 | a = h.attributes; 10 | while (a.length > 0) h.removeAttribute(a[0].name); 11 | } 12 | }); 13 | -------------------------------------------------------------------------------- /docs/articles/making_tables_files/header-attrs-2.11.3/header-attrs.js: -------------------------------------------------------------------------------- 1 | // Pandoc 2.9 adds attributes on both header and div. We remove the former (to 2 | // be compatible with the behavior of Pandoc < 2.8). 3 | document.addEventListener('DOMContentLoaded', function(e) { 4 | var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); 5 | var i, h, a; 6 | for (i = 0; i < hs.length; i++) { 7 | h = hs[i]; 8 | if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 9 | a = h.attributes; 10 | while (a.length > 0) h.removeAttribute(a[0].name); 11 | } 12 | }); 13 | -------------------------------------------------------------------------------- /docs/articles/making_tables_files/header-attrs-2.11/header-attrs.js: -------------------------------------------------------------------------------- 1 | // Pandoc 2.9 adds attributes on both header and div. We remove the former (to 2 | // be compatible with the behavior of Pandoc < 2.8). 3 | document.addEventListener('DOMContentLoaded', function(e) { 4 | var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); 5 | var i, h, a; 6 | for (i = 0; i < hs.length; i++) { 7 | h = hs[i]; 8 | if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 9 | a = h.attributes; 10 | while (a.length > 0) h.removeAttribute(a[0].name); 11 | } 12 | }); 13 | -------------------------------------------------------------------------------- /docs/articles/making_tables_files/header-attrs-2.8/header-attrs.js: -------------------------------------------------------------------------------- 1 | // Pandoc 2.9 adds attributes on both header and div. We remove the former (to 2 | // be compatible with the behavior of Pandoc < 2.8). 3 | document.addEventListener('DOMContentLoaded', function(e) { 4 | var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); 5 | var i, h, a; 6 | for (i = 0; i < hs.length; i++) { 7 | h = hs[i]; 8 | if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 9 | a = h.attributes; 10 | while (a.length > 0) h.removeAttribute(a[0].name); 11 | } 12 | }); 13 | -------------------------------------------------------------------------------- /docs/articles/making_tables_files/tabwid-1.0.0/scrool.css: -------------------------------------------------------------------------------- 1 | .flextable-shadow-host{ 2 | overflow: scroll; 3 | } 4 | -------------------------------------------------------------------------------- /docs/articles/making_tables_files/tabwid-1.0.0/tabwid.css: -------------------------------------------------------------------------------- 1 | .tabwid table{ 2 | border-spacing:0px !important; 3 | border-collapse:collapse; 4 | line-height:1; 5 | margin-left:auto; 6 | margin-right:auto; 7 | border-width: 0; 8 | display: table; 9 | margin-top: 1.275em; 10 | margin-bottom: 1.275em; 11 | border-color: transparent; 12 | } 13 | .tabwid_left table{ 14 | margin-left:0; 15 | } 16 | .tabwid_right table{ 17 | margin-right:0; 18 | } 19 | .tabwid td { 20 | padding: 0; 21 | } 22 | .tabwid a { 23 | text-decoration: none; 24 | } 25 | .tabwid thead { 26 | background-color: transparent; 27 | } 28 | .tabwid tfoot { 29 | background-color: transparent; 30 | } 31 | .tabwid table tr { 32 | background-color: transparent; 33 | } 34 | -------------------------------------------------------------------------------- /docs/articles/making_tables_files/tabwid-1.1.2/tabwid.css: -------------------------------------------------------------------------------- 1 | .tabwid table{ 2 | border-spacing:0px !important; 3 | border-collapse:collapse; 4 | line-height:1; 5 | margin-left:auto; 6 | margin-right:auto; 7 | border-width: 0; 8 | border-color: transparent; 9 | caption-side: top; 10 | } 11 | .tabwid-caption-bottom table{ 12 | caption-side: bottom; 13 | } 14 | .tabwid_left table{ 15 | margin-left:0; 16 | } 17 | .tabwid_right table{ 18 | margin-right:0; 19 | } 20 | .tabwid td, .tabwid th { 21 | padding: 0; 22 | } 23 | .tabwid a { 24 | text-decoration: none; 25 | } 26 | .tabwid thead { 27 | background-color: transparent; 28 | } 29 | .tabwid tfoot { 30 | background-color: transparent; 31 | } 32 | .tabwid table tr { 33 | background-color: transparent; 34 | } 35 | .katex-display { 36 | margin: 0 0 !important; 37 | } 38 | -------------------------------------------------------------------------------- /docs/articles/making_tables_files/tabwid-1.1.3/tabwid.css: -------------------------------------------------------------------------------- 1 | .tabwid { 2 | font-size: initial; 3 | padding-bottom: 1em; 4 | } 5 | 6 | .tabwid table{ 7 | border-spacing:0px !important; 8 | border-collapse:collapse; 9 | line-height:1; 10 | margin-left:auto; 11 | margin-right:auto; 12 | border-width: 0; 13 | border-color: transparent; 14 | caption-side: top; 15 | } 16 | .tabwid-caption-bottom table{ 17 | caption-side: bottom; 18 | } 19 | .tabwid_left table{ 20 | margin-left:0; 21 | } 22 | .tabwid_right table{ 23 | margin-right:0; 24 | } 25 | .tabwid td, .tabwid th { 26 | padding: 0; 27 | } 28 | .tabwid a { 29 | text-decoration: none; 30 | } 31 | .tabwid thead { 32 | background-color: transparent; 33 | } 34 | .tabwid tfoot { 35 | background-color: transparent; 36 | } 37 | .tabwid table tr { 38 | background-color: transparent; 39 | } 40 | .katex-display { 41 | margin: 0 0 !important; 42 | } 43 | -------------------------------------------------------------------------------- /docs/articles/making_tables_files/tabwid-1.1.3/tabwid.js: -------------------------------------------------------------------------------- 1 | document.addEventListener("DOMContentLoaded", function(event) { 2 | var els = document.querySelectorAll(".tabwid"); 3 | var tabwid_link = document.querySelector('link[href*="tabwid.css"]') 4 | if (tabwid_link === null) { 5 | const tabwid_styles = document.evaluate("//style[contains(., 'tabwid')]", document, null, XPathResult.ANY_TYPE, null ); 6 | tabwid_link = tabwid_styles.iterateNext(); 7 | } 8 | 9 | Array.prototype.forEach.call(els, function(template) { 10 | const dest = document.createElement("div"); 11 | template.parentNode.insertBefore(dest, template.nextSibling) 12 | dest.setAttribute("class", "flextable-shadow-host"); 13 | const fantome = dest.attachShadow({mode: 'open'}); 14 | fantome.appendChild(template); 15 | if (tabwid_link !== null) { 16 | fantome.appendChild(tabwid_link.cloneNode(true)); 17 | } 18 | }); 19 | }); 20 | 21 | -------------------------------------------------------------------------------- /docs/articles/package_intro_files/header-attrs-2.8/header-attrs.js: -------------------------------------------------------------------------------- 1 | // Pandoc 2.9 adds attributes on both header and div. We remove the former (to 2 | // be compatible with the behavior of Pandoc < 2.8). 3 | document.addEventListener('DOMContentLoaded', function(e) { 4 | var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); 5 | var i, h, a; 6 | for (i = 0; i < hs.length; i++) { 7 | h = hs[i]; 8 | if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 9 | a = h.attributes; 10 | while (a.length > 0) h.removeAttribute(a[0].name); 11 | } 12 | }); 13 | -------------------------------------------------------------------------------- /docs/articles/package_intro_files/header-attrs-2.9/header-attrs.js: -------------------------------------------------------------------------------- 1 | // Pandoc 2.9 adds attributes on both header and div. We remove the former (to 2 | // be compatible with the behavior of Pandoc < 2.8). 3 | document.addEventListener('DOMContentLoaded', function(e) { 4 | var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); 5 | var i, h, a; 6 | for (i = 0; i < hs.length; i++) { 7 | h = hs[i]; 8 | if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 9 | a = h.attributes; 10 | while (a.length > 0) h.removeAttribute(a[0].name); 11 | } 12 | }); 13 | -------------------------------------------------------------------------------- /docs/bootstrap-toc.css: -------------------------------------------------------------------------------- 1 | /*! 2 | * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) 3 | * Copyright 2015 Aidan Feldman 4 | * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ 5 | 6 | /* modified from 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default, but at >768px, show it */ 37 | padding-bottom: 10px; 38 | } 39 | nav[data-toggle='toc'] .nav .nav > li > a { 40 | padding-top: 1px; 41 | padding-bottom: 1px; 42 | padding-left: 30px; 43 | font-size: 12px; 44 | font-weight: normal; 45 | } 46 | nav[data-toggle='toc'] .nav .nav > li > a:hover, 47 | nav[data-toggle='toc'] .nav .nav > li > a:focus { 48 | padding-left: 29px; 49 | } 50 | nav[data-toggle='toc'] .nav .nav > .active > a, 51 | nav[data-toggle='toc'] .nav .nav > .active:hover > a, 52 | nav[data-toggle='toc'] .nav .nav > .active:focus > a { 53 | padding-left: 28px; 54 | font-weight: 500; 55 | } 56 | 57 | /* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */ 58 | nav[data-toggle='toc'] .nav > .active > ul { 59 | display: block; 60 | } 61 | -------------------------------------------------------------------------------- /docs/deps/bootstrap-5.1.0/font.css: 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| 17 | var referrer = document.URL ; 18 | var paramKey = "q" ; 19 | 20 | if (referrer.indexOf("?") !== -1) { 21 | var qs = referrer.substr(referrer.indexOf('?') + 1); 22 | var qs_noanchor = qs.split('#')[0]; 23 | var qsa = qs_noanchor.split('&'); 24 | var keyword = ""; 25 | 26 | for (var i = 0; i < qsa.length; i++) { 27 | var currentParam = qsa[i].split('='); 28 | 29 | if (currentParam.length !== 2) { 30 | continue; 31 | } 32 | 33 | if (currentParam[0] == paramKey) { 34 | keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20")); 35 | } 36 | } 37 | 38 | if (keyword !== "") { 39 | $(".contents").unmark({ 40 | done: function() { 41 | $(".contents").mark(keyword); 42 | } 43 | }); 44 | } 45 | } 46 | }; 47 | 48 | mark(); 49 | }); 50 | }); 51 | 52 | /* Search term highlighting ------------------------------*/ 53 | 54 | function matchedWords(hit) { 55 | var words = []; 56 | 57 | var hierarchy = hit._highlightResult.hierarchy; 58 | // loop to fetch from lvl0, lvl1, etc. 59 | for (var idx in hierarchy) { 60 | words = words.concat(hierarchy[idx].matchedWords); 61 | } 62 | 63 | var content = hit._highlightResult.content; 64 | if (content) { 65 | words = words.concat(content.matchedWords); 66 | } 67 | 68 | // return unique words 69 | var words_uniq = [...new Set(words)]; 70 | return words_uniq; 71 | } 72 | 73 | function updateHitURL(hit) { 74 | 75 | var words = matchedWords(hit); 76 | var url = ""; 77 | 78 | if (hit.anchor) { 79 | url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; 80 | } else { 81 | url = hit.url + '?q=' + escape(words.join(" ")); 82 | } 83 | 84 | return url; 85 | } 86 | -------------------------------------------------------------------------------- /docs/favicon-16x16.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/docs/favicon-16x16.png 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https://higgi13425.github.io/medicaldata/articles/package_intro.html 29 | 30 | 31 | https://higgi13425.github.io/medicaldata/authors.html 32 | 33 | 34 | https://higgi13425.github.io/medicaldata/index.html 35 | 36 | 37 | https://higgi13425.github.io/medicaldata/news/index.html 38 | 39 | 40 | https://higgi13425.github.io/medicaldata/reference/abm.html 41 | 42 | 43 | https://higgi13425.github.io/medicaldata/reference/blood_storage.html 44 | 45 | 46 | https://higgi13425.github.io/medicaldata/reference/cath.html 47 | 48 | 49 | https://higgi13425.github.io/medicaldata/reference/cdystonia.html 50 | 51 | 52 | https://higgi13425.github.io/medicaldata/reference/covid_testing.html 53 | 54 | 55 | https://higgi13425.github.io/medicaldata/reference/cytomegalovirus.html 56 | 57 | 58 | https://higgi13425.github.io/medicaldata/reference/diabetes.html 59 | 60 | 61 | https://higgi13425.github.io/medicaldata/reference/esoph.html 62 | 63 | 64 | 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\item{site}{study site, levels: 1-9; type: double} 12 | \item{id}{patient id at each site (not unique across sites); type: numeric 1-19} 13 | \item{treat}{treatment assignment: levels: 1 = placebo, 2 = botox B 5000 units, 3 = botox B 10,000 units; type: double 1-3)} 14 | \item{age}{age in years, range 26-83; type: numeric} 15 | \item{sex}{sex of participant, 1 = F, 2 = M; type: double} 16 | \item{twstrs}{total score of Toronto Western Spasmodic Torticollis Rating Scale, range 0-87; type: numeric} 17 | } 18 | } 19 | \source{ 20 | This data set is from a study published in 1999 in the journal \emph{Neurology},\cr 21 | \cr 22 | A. Brashear, M.F. Lew, D.D. Dykstra, C.L. Comella, S.A. Factor, R.L. Rodnitzky, R. Trosch, C. Singer, M.F. Brin, J.J. Murray, J.D. Wallace, A. Willmer–Hulme, M. Koller (1999), \emph{Safety and efficacy of NeuroBloc (botulinum toxin type B) in type A–responsive cervical dystonia}. Neurology, 53(7), 1439. 23 | 24 | This dataset has been passed through Statistical Methods for the Analysis of Repeated Measurements by Charles S. Davis, pp. 161-163 (Springer, 2002) and can also be found at Frank Harrell's website \href{https://hbiostat.org/data/}{here}. 25 | } 26 | \usage{ 27 | cdystonia 28 | } 29 | \description{ 30 | Longitudinal results of a randomized, placebo-controlled trial of botulinum toxin type B (BotB) in 109 subjects across 9 sites. (\strong{more details} available below the variable definitions). \cr 31 | } 32 | \details{ 33 | Cervical dystonia, also called spasmodic torticollis, is a painful condition in which your neck muscles contract involuntarily, causing your head to twist or turn to one side. Cervical dystonia can also cause your head to uncontrollably tilt forward or backward. \cr 34 | A rare disorder that can occur at any age, cervical dystonia most often occurs in middle-aged people, women more than men. Symptoms generally begin gradually and then reach a point where they don't get substantially worse.\cr 35 | 109 subjects across 9 sites were randomized to placebo (N = 36), 5000 units of botulinum toxin B (N = 36), or 10,000 units of botulinum toxin B (N = 37), injected once at baseline into the affected muscle to partially paralyze it and make it relax, releasing the spasmed side of the neck and head.\cr 36 | The response variable is the score on the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS-Total on a 0-87 scale), which measures the severity, pain, and disability of cervical dystonia (higher scores mean more impairment) at weeks 0 (baseline), 2, 4, 8, 12, and 16. It is expected that the single botox injection at week 0 may wear off over time. 37 | } 38 | \keyword{datasets} 39 | -------------------------------------------------------------------------------- /man/codebooks/cath_codebook.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Cath data codebook" 3 | author: "Peter Higgins" 4 | date: "10/30/2021" 5 | output: 6 | html_document: 7 | theme: flatly 8 | --- 9 | 10 | ```{r setup, include=FALSE} 11 | knitr::opts_chunk$set(echo = TRUE) 12 | library(tidyverse) 13 | library(flextable) 14 | ``` 15 | 16 | 17 | ## Cath Dataset Codebook 18 | 19 | Source: This data set is generously donated from Frank Harrell, and is originally from the Duke Cardiovascular Disease Databank. 20 | 21 | ### Codebook 22 | 23 | ```{r, echo=FALSE, message=FALSE} 24 | cath_codebook <- tribble( 25 | ~variable, ~position, ~Variable_Label, ~units, ~Codes, ~type, 26 | "sex", 1, "Gender", NA, "0 = male, 1 = female", "double", 27 | "age", 2, "Age", "years", NA, "double", 28 | "cad_dur", 3, "duration of cadiac symptoms", "days", NA, "double", 29 | "choleste", 4, "Serum Cholesterol", "milligrams per deciliter", NA, "double", 30 | "sigdz", 5, "Significant Coronary Artery Disease Found on Cardiac Cath", NA, "0 = no, 1 = yes", "double", 31 | "tvdlm", 6, "Three Vessel or Left Main Disease Found on Cardiac Cath", NA, "0 = no, 1 = yes", "double", 32 | ) %>% 33 | flextable() %>% 34 | fontsize(size = 11) %>% 35 | width(width = c(1.75, 0.5, 1.25, 0.35, 1.25, 0.5)) 36 | 37 | cath_codebook 38 | 39 | ``` 40 | -------------------------------------------------------------------------------- /man/codebooks/cdystonia_codebook.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Cervical Dystonia RCT data codebook" 3 | author: "Peter Higgins" 4 | date: "10/18/2020" 5 | output: 6 | html_document: 7 | theme: flatly 8 | --- 9 | 10 | ```{r setup, include=FALSE} 11 | knitr::opts_chunk$set(echo = TRUE) 12 | library(tidyverse) 13 | library(flextable) 14 | ``` 15 | 16 | 17 | ## Cervical Dystonia Dataset Codebook 18 | 19 | Source:
20 | This data set is from a study published in 1999 in the journal Neurology, 21 | 22 | A. Brashear, M.F. Lew, D.D. Dykstra, C.L. Comella, S.A. Factor, R.L. Rodnitzky, R. Trosch, C. Singer, M.F. Brin, J.J. Murray, J.D. Wallace, A. Willmer–Hulme, M. Koller (1999), Safety and efficacy of NeuroBloc (botulinum toxin type B) in type A–responsive cervical dystonia. Neurology, 53(7), 1439. 23 | 24 | This dataset has been passed through Statistical Methods for the Analysis of Repeated Measurements by Charles S. Davis, pp. 161-163 (Springer, 2002) and can also be found at Frank Harrell's website here. 25 | 26 | ### Codebook 27 | 28 | ```{r, echo=FALSE, message=FALSE} 29 | cdystonia_codebook <- tribble( 30 | ~variable, ~position, ~Variable_Label, ~units, ~Codes, ~type, 31 | "week", 1, "Week of Observation, 0-16", NA, NA, "numeric", 32 | "site", 2, "Study Site, 1-9", NA, NA, "double", 33 | "id", 3, "patient id at each site (not unique across sites); 1-19", NA , NA, "numeric", 34 | "treat", 4, "treatment assignment with levels: 1 = placebo, 2 = BotB5000, 3 = BotB10000", NA, NA, "double", 35 | "age", 5, "Age", "years", NA, "numeric", 36 | "sex", 6, "sex of participant, 1 = F, 2 = M", NA, NA, "double", 37 | "twstrs", 7, "total score of Toronto Western Spasmodic Torticollis Rating Scale, range 0-87", NA, NA, "numeric" 38 | ) %>% 39 | flextable() %>% 40 | fontsize(size = 11) %>% 41 | width(width = c(1, 0.5, 2, 0.5, 1.25, 0.5)) 42 | cdystonia_codebook 43 | ``` -------------------------------------------------------------------------------- /man/codebooks/covid_testing_codebook.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "COVID-19 Testing data codebook" 3 | author: "Peter Higgins" 4 | date: "10/18/2020" 5 | output: 6 | html_document: 7 | theme: flatly 8 | --- 9 | 10 | ```{r setup, include=FALSE} 11 | knitr::opts_chunk$set(echo = TRUE) 12 | library(tidyverse) 13 | library(flextable) 14 | ``` 15 | 16 | 17 | ## COVID-19 Testing Dataset Codebook 18 | 19 | Source: This data set is from Amrom E. Obstfeld, who de-identified data on COVID-19 testing during 2020 at CHOP (Children's Hospital of Pennsylvania). 20 | 21 | This data set contains data concerning testing for SARS-CoV2 via PCR as well as associated metadata. The data has been anonymized, time-shifted, and permuted 22 | 23 | ### Codebook 24 | 25 | ```{r, echo=FALSE, message=FALSE} 26 | covid_codebook <- read_csv("/Users/peterhiggins/Documents/RCode/medicaldata/data-raw/covid_testing_code_book.csv") %>% 27 | slice(1:17) %>% 28 | flextable() %>% 29 | fontsize(size = 12) %>% 30 | width(width = c(1, 0.5, 2.5, 0.5, 2)) 31 | 32 | covid_codebook 33 | 34 | ``` 35 | 36 | 37 | -------------------------------------------------------------------------------- /man/codebooks/diabetes_codebook.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Diabetes data codebook" 3 | author: "Peter Higgins" 4 | date: "7/24/2021" 5 | output: 6 | html_document: 7 | theme: flatly 8 | --- 9 | 10 | ```{r setup, include=FALSE} 11 | knitr::opts_chunk$set(echo = TRUE) 12 | library(tidyverse) 13 | library(flextable) 14 | ``` 15 | 16 | 17 | ## Diabetes Dataset Codebook 18 | 19 | Source: This data set was provided through funding from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) as the dataset "diabetes". The donor of dataset to UCI was Vincent Sigillito of Johns Hopkins. A version of this dataset is available through the UCI (University of California-Irvine) Machine Learning Repository as "PimaIndiansDiabetes". 20 | 21 | Type 2 diabetes mellitus (DM) is associated with obesity and a diet high in sugars and low in vegetables. People with type 2 DM become less sensitive to insulin, so that after a glucose load, their blood glucose and insulin rise, but the glucose level does not fall as quickly as it should, leading to sustained elevations in glucose and insulin. The incidence of type 2 DM is rising in many western cultures, as increasingly unhealthy and calorie rich diets become common. A version of this dataset is available through the UCI (University of California-Irvine) Machine Learning Repository as "PimaIndiansDiabetes". This dataset was recoded with NA for zero values which were likely to be missing in the variables glucose_mg-dl, insulin_mIU-mL, dbp_mm-hg, triceps_mm, and bmi by Friedrich Leisch. The units of each predictor were added to variable names, and several variables renamed for clarity by Peter Higgins. 22 | 23 | The primary analysis task is to classify in each participant whether diabetes developed within 5 years of data collection (diabetes_5y = pos), or the participant tested repeatedly negative for diabetes over the next 5 years (diabetes_5y = neg). 24 | 25 | ### Codebook 26 | 27 | ```{r, echo=FALSE, message=FALSE} 28 | diabetes_codebook <- tribble( 29 | ~variable, ~position, ~Variable_Label, ~units, ~Codes, ~type, 30 | "pregnancy_num", 1, "number of pregnancies", NA, "1-17", "double", 31 | "glucose_mg-dl", 2, "Plasma glucose concentration at 2 hours after administration of an oral glucose tolerance test", "mg/deciliter", NA, "double", 32 | "dbp_mm-hg", 3, "Dose of Theophylline in milligrams per kilogram of body weight", "mg/kg", NA, "double", 33 | "triceps_mm", 4, "triceps skin fold thickness in mm, a measure of subcutaneous fat", "mm", NA, "double", 34 | "insulin_microiu-ml", 4, "serum insulin at 2 hours after administration of an oral glucose tolerance test in microIU per milliliter (IU is international units)", "microIU/mL", NA, "double", 35 | "bmi", 5, "body mass index, in kg of weight per meters of height squared, from 18.2 to 67.1", "microIU/mL", NA, "double", 36 | "pedigree", 6, "a diabetes pedigree score, with points added for each additional relative with diabetes, weighted for the closeness of their genetic relation to the participant, from 0.78 to 2.42", "microIU/mL", NA, "double", 37 | "age", 7, "Age in years, from 21 to 81", "microIU/mL", NA, "double", 38 | "diabetes_5y", 8, "Diagnosis of diabetes in the following 5 years - pos or neg", NA, "pos or neg", "factor", 39 | ) %>% 40 | flextable() %>% 41 | fontsize(size = 11) %>% 42 | width(width = c(1, 0.5, 3, 1, 0.25, 1)) 43 | diabetes_codebook 44 | ``` 45 | -------------------------------------------------------------------------------- /man/codebooks/esoph_ca_codebook.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Esophageal Cancer data codebook" 3 | author: "Peter Higgins" 4 | date: "8/10/2021" 5 | output: 6 | html_document: 7 | theme: flatly 8 | --- 9 | 10 | ```{r setup, include=FALSE} 11 | knitr::opts_chunk$set(echo = TRUE) 12 | library(tidyverse) 13 | library(flextable) 14 | ``` 15 | 16 | 17 | ### Introduction 18 | 19 | This dataset is from a case-control study of esophageal cancer in Ille-et-Vilaine, France, evaluating the effects of smoking and alcohol on the incidence of esophageal cancer. Smoking and alcohol are associated risk factors for squamous cell cancer of the esophagus, rather than adenocarcinoma of the esophagus, which is associated with obesity and esophageal reflux. This is an original base R dataset. 20 | 21 | ### Codebook 22 | 23 | ```{r, echo=FALSE, message=FALSE} 24 | esoph_codebook <- tribble( 25 | ~variable, ~position, ~Variable_Label, ~units, ~Codes, ~type, 26 | "agegp", 1, "Age Group", NA, "6 levels of age: 25-34, 35-44, 45-54, 55-64, 65-74, 75+", "ordinal factor", 27 | "alcgp", 2, "Age Group", NA, "4 levels of alcohol consumption: 0-39g/day, 40-79, 80-119, 120+", "ordinal factor", 28 | "tobgp", 3, "Tobacco Group", NA, "4 levels of tobacco consumption: 0-9g/day, 10-19, 20-29, 30+", "ordinal factor", 29 | "ncases", 4, "Number of Cases", NA, NA, "integer", 30 | "ncontrols", 5, "Number of Controls", NA, NA, "integer", 31 | ) %>% 32 | flextable() %>% 33 | fontsize(size = 11) %>% 34 | width(width = c(1, 0.75, 1, 0.5, 2.5, 1)) 35 | esoph_codebook 36 | ``` 37 | -------------------------------------------------------------------------------- /man/codebooks/indometh_codebook.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Indomethacin data codebook" 3 | author: "Peter Higgins" 4 | date: "7/24/2021" 5 | output: 6 | html_document: 7 | theme: flatly 8 | --- 9 | 10 | ```{r setup, include=FALSE} 11 | knitr::opts_chunk$set(echo = TRUE) 12 | library(tidyverse) 13 | library(flextable) 14 | ``` 15 | 16 | 17 | ## Indomethacin Dataset Codebook 18 | 19 | Source: This data set is from a pharmacokinetic study of intravenous dosing of the anti-inflammatory and pain relieving medication, indomethacin, in 6 subjects, published in 1976 in the Journal of Pharmacokinetics and Biopharmaceutics. 20 | 21 | Kwan, Breault, Umbenhauer, McMahon and Duggan (1976) Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Pharmacokinetics and Biopharmaceutics. 1976 Jun;4(3):255-80. doi: 10.1007/BF01063617. 22 | 23 | This dataset is is one of the core datasets in base R. It can be used for examples of line plots over time, grouped by subject, and non-linear modeling, including general additive models (GAMs). 24 | 25 | ### Codebook 26 | 27 | ```{r, echo=FALSE, message=FALSE} 28 | indometh_codebook <- tribble( 29 | ~variable, ~position, ~Variable_Label, ~units, ~Codes, ~type, 30 | "subject", 1, "Subject ID", NA, NA, "character", 31 | "time", 2, "Time of plasma sampling", "hours", NA, "double", 32 | "conc", 3, "Concentration of Indomethacin in the plasma", "mcg/mL", NA, "double" 33 | ) %>% 34 | flextable() %>% 35 | fontsize(size = 11) %>% 36 | width(width = c(1, 0.5, 3, 0.5, 0.5, 0.75)) 37 | indometh_codebook 38 | ``` 39 | -------------------------------------------------------------------------------- /man/codebooks/polyps_codebook.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Polyps data codebook" 3 | author: "Peter Higgins" 4 | date: "10/18/2020" 5 | output: 6 | html_document: 7 | theme: flatly 8 | --- 9 | 10 | ```{r setup, include=FALSE} 11 | knitr::opts_chunk$set(echo = TRUE) 12 | library(tidyverse) 13 | library(flextable) 14 | ``` 15 | 16 | 17 | ## Polyps Dataset Codebook 18 | 19 | Source: This data set is from a study published in 1993 in the New England Journal of Medicine, 20 | 21 | F. M. Giardiello, S. R. Hamilton, A. J. Krush, S. Piantadosi, L. M. Hylind, P. Celano, S. V. Booker, C. R. Robinson and G. J. A. Offerhaus (1993), Treatment of colonic and rectal adenomas with sulindac in familial adenomatous polyposis. New England Journal of Medicine, 328(18), 1313–1316. 22 | 23 | This dataset is derived from and improved upon from polyps and polyps3 in the {HSAUR} package, which divides up the dataset to different times, neglects the NAs, and does not include participant IDs. Consulting the original paper was quite helpful in reconstructing this dataset. 24 | 25 | ### Codebook 26 | 27 | ```{r, echo=FALSE, message=FALSE} 28 | polyps_codebook <- tribble( 29 | ~variable, ~position, ~Variable_Label, ~units, ~Codes, ~type, 30 | "participant_id", 1, "Participant ID", NA, NA, "character", 31 | "sex", 2, "Sex", NA, "male, female", "factor", 32 | "age", 3, "Age", "years", NA, "numeric", 33 | "baseline", 4, "Number of Polyps at Baseline", NA, NA, "numeric", 34 | "treatment", 5, "Treatment Assigned", NA, "sulindac, placebo", "factor", 35 | "number3m", 6, "Number of Polyps at 3 Months", NA, NA, "numeric", 36 | "number12m", 7, "Number of Polyps at 12 Months", NA, NA, "numeric" 37 | ) %>% 38 | flextable() %>% 39 | fontsize(size = 11) %>% 40 | width(width = c(1, 0.5, 2, 0.5, 1.25, 0.5)) 41 | polyps_codebook 42 | ``` -------------------------------------------------------------------------------- /man/codebooks/scurvy_codebook.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Scurvy data codebook" 3 | author: "Peter Higgins" 4 | date: "6/24/2021" 5 | output: 6 | html_document: 7 | theme: flatly 8 | --- 9 | 10 | ```{r setup, include=FALSE} 11 | knitr::opts_chunk$set(echo = TRUE) 12 | library(tidyverse) 13 | library(flextable) 14 | ``` 15 | 16 | 17 | ## Scurvy Dataset Codebook 18 | 19 | Source: This data set is from a study published in 1757, _A Treatise on the Scurvy_, by James Lind. 20 | 21 | The dataset is derived directly from pages 149-153 of this tome, which includes the description of a small, randomized, comparator-controlled 6 day clinical trial of 6 scurvy remedies in 12 seamen with scurvy. The particpant_id variable, and the Likert scales for each outcome measured in this reconstructed dataset, are a bit fanciful (as Likert was not even born in 1757), but match Lind's descriptions. You can read the original study in a scanned volume on Google Books [here](https://www.google.com/books/edition/A_Treatise_on_the_Scurvy/oP1UEXWU7fsC?hl=en&gbpv=1&printsec=frontcover). 22 | 23 | ### Codebook 24 | 25 | ```{r, echo=FALSE, message=FALSE} 26 | scurvy_codebook <- tribble( 27 | ~variable, ~position, ~Variable_Label, ~units, ~Codes, ~type, 28 | "study_id", 1, "Participant ID", NA, NA, "character", 29 | "treatment", 2, "Treatment", NA, "cider, dilute_sulfuric_acid, vinegar, sea_water, citrus, purgative_mixture", "factor", 30 | "dosing_regimen_for_scurvy", 3, "Dosing Regimen", NA, "1 quart per day; 25 drops of elixir of vitriol, three times a day; two spoonfuls, three times daily; half pint daily; two lemons and an orange daily; a nutmeg-sized paste of garlic, mustard seed, horseradish, balsam of Peru, and gum myrrh three times a day", "factor", 31 | "gum_rot_d6", 4, "Gum Rot on Day 6", NA, "0_none, 1_mild, 2_moderate, 3_severe", "factor", 32 | "skin_sores_d6", 5, "Skin Sores on Day 6", NA, "0_none, 1_mild, 2_moderate, 3_severe", "factor", 33 | "weakness_of_the_knees_d6", 6, "Weakness of the Knees on Day 6", NA, "0_none, 1_mild, 2_moderate, 3_severe", "factor", 34 | "lassitude_d6", 7, "Lassitude on Day 6", NA, "0_none, 1_mild, 2_moderate, 3_severe", "factor", 35 | "fit_for_duty_d6", 8, "Fit for Duty on Day 6", NA, "0_no, 1_yes", "factor", 36 | ) %>% 37 | flextable() %>% 38 | fontsize(size = 11) %>% 39 | width(width = c(1.75, 0.5, 1.25, 0.35, 1.25, 0.5)) 40 | scurvy_codebook 41 | ``` -------------------------------------------------------------------------------- /man/codebooks/strep_tb_codebook.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Streptomycin for Tuberculosis data codebook" 3 | author: "Peter Higgins" 4 | date: "6/24/2021" 5 | output: 6 | html_document: 7 | theme: flatly 8 | --- 9 | 10 | ```{r setup, include=FALSE} 11 | knitr::opts_chunk$set(echo = TRUE) 12 | library(tidyverse) 13 | library(flextable) 14 | ``` 15 | 16 | 17 | ## Streptomycin for Tuberculosis Dataset Codebook 18 | 19 | Source: This data set is from a prospective, randomized, placebo-controlled clinical trial, _Streptomycin Treatment of Pulmonary Tuberculosis_ published on October 30, 1948 in the British Medical Journal, pages 769-782, by the Streptomycin in Tuberculosis Trials Committee. This is often considered the first modern randomized clinical trial ever published. 20 | 21 | This dataset is reconstructed from the article to the best of my ability, and particularly from tables 1-4, which can be reconstructed exactly with janitor::tabyl(), or a more modern Table 1 can be created with gtsummary::tbl_summary(). The _particpant_id_ variable was added to this reconstructed dataset. The _radnum_ variable and the _improved_ variable were added to allow for multivariate and logistic regression modeling (though you can also use logistic regression for the dichotomous outcome of death, radnum =1). You can read the original study in a the BMJ [here](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2091872/pdf/brmedj03701-0007.pdf). 22 | 23 | ### Codebook 24 | 25 | ```{r, echo=FALSE, message=FALSE} 26 | strep_tb_codebook <- tribble( 27 | ~variable, ~position, ~Variable_Label, ~units, ~Codes, ~type, 28 | "patient_id", 1, "Participant ID", NA, NA, "character", 29 | "arm", 2, "Study Arm", NA, "Streptomycin, Control", "factor", 30 | "dose_strep_g", 3, "Dose of Streptomycin in Grams", "grams", NA, "numeric", 31 | "dose_PAS_g", 4, "Dose of Para-Amino-Salicylate in Grams", "grams", NA, "numeric", 32 | "gender", 5, "Gender", NA, "M = Male, F = Female", "factor", 33 | "baseline_condition", 6, "Condition of the Patient at Baseline", NA, "1_Good, 2_Fair, 3_Poor", "factor", 34 | "baseline_temp", 7, "Oral Temperature at Baseline", "Degrees F", "1_<=98.9F/37.2C, 2_99-99.9F/37.3-37.7C, 3_100-100.9F/37.8-38.2C, 4_>=101F/38.3C", "factor", 35 | "baseline_esr", 8, "Erythrocyte Sedimentation Rate at baseline", "millimeters per hour", "1_1-10, 2_11-20, 3_21-50, 4_51+", "factor", 36 | "baseline_cavitation", 9, "Cavitation of the Lungs on chest X-ray at baseline", NA, "yes, no", "factor", 37 | "strep_resistance", 10, "Resistance to Streptomycin at 6m", NA, "1_sens_0-8, 2_mod_8-99, 3_resist_100+", "factor", 38 | "radiologic_6m", 11, "Radiologic outcome at 6m", NA, "1_Death, 2_Considerable Deterioration, 3_Moderate_deterioration, 4_No_change, 5_Moderate_improvement, 6_Considerable_improvement", "factor", 39 | "radnum", 12, "Numeric Rating of Chest X-ray at month 6", NA, "1-6", "numeric", 40 | "improved", 13, "Dichotomous Outcome of Improved", NA, "TRUE, FALSE", "logical", 41 | ) %>% 42 | flextable() %>% 43 | fontsize(size = 11) %>% 44 | width(width = c(1, 0.5, 1.25, 1, 1.25, 0.5)) 45 | 46 | strep_tb_codebook 47 | 48 | ``` 49 | 50 | -------------------------------------------------------------------------------- /man/codebooks/theoph_codebook.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Theophylline Pharmacokinetics data codebook" 3 | author: "Peter Higgins" 4 | date: "7/24/2021" 5 | output: 6 | html_document: 7 | theme: flatly 8 | --- 9 | 10 | ```{r setup, include=FALSE} 11 | knitr::opts_chunk$set(echo = TRUE) 12 | library(tidyverse) 13 | library(flextable) 14 | ``` 15 | 16 | 17 | ## Theophylline Pharmacokinetics Dataset Codebook 18 | 19 | Source: This data set is from a pharmacokinetic study of a single oral dose of the anti-asthma medication, theophylline, in 12 subjects, published around 1980 by Robert A. Upton. Blood was drawn at 11 time points over the next 25 hours to determine the plasma concentration of theophylline. 20 | 21 | This dataset was used for a statistical textbook, without citing the original study, or crediting the original authors for collecting the data. 22 | 23 | This dataset is is one of the core datasets package in base R. It can be used for examples of line plots over time, grouped by subject, and non-linear modeling, including general additive models (GAMs). 24 | 25 | ### Codebook 26 | 27 | ```{r, echo=FALSE, message=FALSE} 28 | theoph_codebook <- tribble( 29 | ~variable, ~position, ~Variable_Label, ~units, ~Codes, ~type, 30 | "Subject", 1, "Subject ID", NA, NA, "ordinal factor", 31 | "Wt", 2, "Subject Weight", "kg", NA, "double", 32 | "Dose", 3, "Dose of Theophylline in milligrams per kilogram of body weight", "mg/kg", NA, "double", 33 | "Time", 4, "Time after dosing", "hours", NA, "double", 34 | "conc", 4, "Concentration of Theophylline in the plasma in micrograms per milliliter", "mcg/mL", NA, "double" 35 | ) %>% 36 | flextable() %>% 37 | fontsize(size = 11) %>% 38 | width(width = c(1, 0.5, 3, 1, 0.25, 1)) 39 | theoph_codebook 40 | ``` 41 | -------------------------------------------------------------------------------- /man/codebooks/thiomon_codebook.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Thiomon data codebook" 3 | author: "Peter Higgins" 4 | date: "7/24/2021" 5 | output: 6 | html_document: 7 | theme: flatly 8 | --- 9 | 10 | ```{r setup, include=FALSE} 11 | knitr::opts_chunk$set(echo = TRUE) 12 | library(tidyverse) 13 | library(flextable) 14 | ``` 15 | 16 | 17 | ## Thiomon (Thiopurine Monitoring) Dataset Codebook 18 | 19 | Source: This data set is from Akbar K. Waljee and Peter D. Higgins, who de-identified data on CBC and chemistry testing at the University of Michigan for development of a machine learning algorithm to predict response to thiopurine medications in IBD patients. This data set contains individual laboratory values, age in days, and outcome (active or remission). These data have been anonymized and time-shifted. As published in Clin Gastroenterol Hepatol. 2010 Feb;8(2):143-150. 20 | 21 | ### Codebook 22 | 23 | ```{r, echo=FALSE, message=FALSE} 24 | thiomon_codebook <- tribble( 25 | ~variable, ~position, ~Variable_Label, ~units, ~Codes, ~type, 26 | "days_of_life", 1, "age in days", NA, NA, "double", 27 | "plt", 2, "Platelet Count", "billions of platelets per Liter", NA, "double", 28 | "mpv", 3, "Mean Platelet Volume", "femtoLiters", NA, "double", 29 | "un", 4, "blood urea nitrogen", "mg/dL", NA, "double", 30 | "wbc", 5, "White blood cell count", "billions of cells per Liter", NA, "double", 31 | "hgb", 6, "hemoglobin", "grams/deciliter", NA, "double", 32 | "hct", 7, "Hematocrit", "cellular percent", NA, "double", 33 | "rbc", 8, "Red Blood cell Count", "billions of cells per Liter", NA, "double", 34 | "mcv", 9, "Mean Corpuscular (RBC) Volume", "femtoliters", NA, "double", 35 | "mch", 10, "Mean Corpuscular (RBC) Hemoglobin", "picograms/cell", NA, "double", 36 | "mchc", 11, "Mean Corpuscular (RBC) Hemoglobin", "grams/deciliter of red cells", NA, "double", 37 | "rdw", 12, "Red cell Distribution Width", "percent", NA, "double", 38 | "neut_percent", 13, "Neutrophil Percent", "percent", NA, "double", 39 | "lymph_percent", 14, "Lymphocyte Percent", "percent", NA, "double", 40 | "mono_percent", 15, "Monocyte Percent", "percent", NA, "double", 41 | "eos_percent", 16, "Eosinophil Percent", "percent", NA, "double", 42 | "baso_percent", 17, "Basophil Percent", "percent", NA, "double", 43 | "sod", 18, "Sodium in plasma", "milliequivalents per Liter", NA, "double", 44 | "pot", 19, "Potassium in plasma", "milliequivalents per Liter", NA, "double", 45 | "chlor", 20, "Chloride in plasma", "milliequivalents per Liter", NA, "double", 46 | "co2", 21, "Bicarbonate in plasma", "milliequivalents per Liter", NA, "double", 47 | "creat", 22, "Creatinine in plasma", "milligrams per deciLiter", NA, "double", 48 | "gluc", 23, "Glucose in plasma", "milligrams per deciliter", NA, "double", 49 | "cal", 24, "Calcium in plasma", "milligrams per deciliter", NA, "double", 50 | "prot", 25, "Protein in plasma", "grams per deciliter", NA, "double", 51 | "alb", 26, "Albumin in plasma", "grams per deciliter", NA, "double", 52 | "ast", 27, "Aspartate Transaminase in plasma", "units per Liter", NA, "double", 53 | "alt", 28, "Alanine Transaminase in plasma", "units per Liter", NA, "double", 54 | "alk", 29, "Alkaline phosphatase in plasma", "units per Liter", NA, "double", 55 | "tbil", 30, "Total Bilirubin in plasma", "milligrams per deciliter", NA, "double", 56 | "active", 31, "Active Inflammation despite Thiopurines for > 12 weeks", NA, "0-1", "double", 57 | "remission", 32, "Remission of Inflammation after Thiopurines for > 12 weeks", NA, "0-1", "double", 58 | ) %>% 59 | flextable() %>% 60 | fontsize(size = 11) %>% 61 | width(width = c(1, 0.5, 3, 1, 0.25, 1)) 62 | thiomon_codebook 63 | ``` 64 | -------------------------------------------------------------------------------- /man/covid_testing.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/covid_testing.R 3 | \docType{data} 4 | \name{covid_testing} 5 | \alias{covid_testing} 6 | \title{Deidentified Results of COVID-19 testing at the Children's Hospital of Pennsylvania (CHOP) in 2020} 7 | \format{ 8 | A data frame with 15524 observations and 17 variables 9 | \describe{ 10 | \item{subject_id}{id number for each subject; type: numeric} 11 | \item{fake_first_name}{an auto-generated fake first name; type: character} 12 | \item{fake_last_name}{an auto-generated fake last name; character} 13 | \item{gender}{anonymized Gender, levels: female, male; type: character} 14 | \item{pan_day}{day after start of pandemic; type: numeric} 15 | \item{test_id}{test that was performed, levels: covid, xcvd1; type: character} 16 | \item{clinic_name}{Clinic or ward where the specimen was collected, 88 levels; type: character} 17 | \item{result}{result of test, levels: positive, negative, invalid; type: character} 18 | \item{demo_group}{patient group, levels: patient, misc_adult, client, other adult, unidentified; type: character} 19 | \item{age}{Age of subject at time of specimen collection (Anonymized), units = years; type: numeric} 20 | \item{drive_thru_ind}{Whether the specimen was collected via a drive-thru site, levels: 1: Collected at drive-thru site; 0: Not collected at drive-thru site; type: numeric} 21 | \item{ct_result}{Cycle at which threshold reached during PCR, range: 14.05-45; type: numeric} 22 | \item{orderset}{Whether an order set was used for test order, levels: 1: Collected via orderset; 0: Not collected via orderset; numeric} 23 | \item{payor_group}{Payor associated with order, levels: commercial, government, unassigned, medical assistance, self pay, charity care, other; type: character} 24 | \item{patient_class}{Disposition of subject at time of collection, levels: inpatient, emergency, observation, recurring outpatient, outpatient, not applicable, day surgery, admit after surgery-obs, admit after surgery-ip; type: character} 25 | \item{col_rec_tat}{Time elapsed between collect time and receive time, range: 0 - 61370.2, units = hours; type: numeric} 26 | \item{rec_ver_tat}{Time elapsed between receive time and verification time, range: -18.6 - 218.2, units = hours; type: numeric} 27 | ... 28 | } 29 | } 30 | \source{ 31 | This data set is from Amrom E. Obstfeld, who de-identified data on COVID-19 testing during 2020 at CHOP (Children's Hospital of Pennsylvania). This data set contains data concerning testing for SARS-CoV2 via PCR as well as associated metadata. These data have been anonymized, time-shifted, and permuted. 32 | } 33 | \usage{ 34 | covid_testing 35 | } 36 | \description{ 37 | A dataset containing details of SARS-CoV-2 testing in 2020 at CHOP 38 | } 39 | \details{ 40 | Data on testing for SARS-CoV2 from days 4-107 of the COVID pandemic in 2020. CHOP is a pediatric hospital in Philadelphia, Pennsylvania, USA. These data have been anonymized, time-shifted, and permuted. 41 | } 42 | \keyword{datasets} 43 | -------------------------------------------------------------------------------- /man/description_docs/cdystonia_desc.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Botulinum Toxin B vs Placebo RCT for Cervical Dystonia Dataset Description Document" 3 | author: "Peter Higgins" 4 | date: "4/5/2023" 5 | output: 6 | html_document: 7 | theme: cerulean 8 | --- 9 | 10 | ```{r setup, include=FALSE} 11 | knitr::opts_chunk$set(echo = TRUE) 12 | ``` 13 | 14 | ## Source: 15 | 16 | This data set is from a study published in 1999 in the journal Neurology, 17 | 18 | A. Brashear, M.F. Lew, D.D. Dykstra, C.L. Comella, S.A. Factor, R.L. Rodnitzky, R. Trosch, C. Singer, M.F. Brin, J.J. Murray, J.D. Wallace, A. Willmer–Hulme, M. Koller (1999), Safety and efficacy of NeuroBloc (botulinum toxin type B) in type A–responsive cervical dystonia. Neurology, 53(7), 1439. 19 | 20 | This dataset has been passed through Statistical Methods for the Analysis of Repeated Measurements by Charles S. Davis, pp. 161-163 (Springer, 2002) and can also be found at Frank Harrell's website here. 21 | 22 | ### Abstract 23 | 24 | Longitudinal results of a randomized, placebo-controlled trial of botulinum toxin type B (BotB) in 109 subjects across 9 sites. (more details available below the variable definitions). 25 | 26 | ### Background 27 | 28 | Cervical dystonia, also called spasmodic torticollis, is a painful condition in which your neck muscles contract involuntarily, causing your head to twist or turn to one side. Cervical dystonia can also cause your head to uncontrollably tilt forward or backward. 29 | A rare disorder that can occur at any age, cervical dystonia most often occurs in middle-aged people, women more than men. Symptoms generally begin gradually and then reach a point where they don't get substantially worse. 30 | 109 subjects across 9 sites were randomized to placebo (N = 36), 5000 units of botulinum toxin B (N = 36), or 10,000 units of botulinum toxin B (N = 37), injected into the affected muscle to partially paralyze it and make it relax, releasing the spasmed side of the neck and head. 31 | The response variable is the score on the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS-Total on a 0-87 scale), which measures the severity, pain, and disability of cervical dystonia (higher scores mean more impairment) at weeks 0 (baseline), 2, 4, 8, 12, and 16. It is expected that the single botox injection at week 0 may wear off over time. 32 | 33 | ### Study Design 34 | 35 | Prospective randomized controlled trial of two doses of botulinum toxin B vs. placebo in the treatment of cervical dystonia (torticollis. 36 | 37 | ### Subjects & Variables 38 | 39 | 109 participants across 9 sites were assigned to 1 of 2 doses of botox B, 5000 or 10,000 units injected into the affected neck muscles, or a placebo injection, and followed for 16 weeks. 40 | 41 | 7 variables 42 | 43 | - week (0-16) 44 | - site (1-9) 45 | - id (1-19, not unique across sites) 46 | - treat (1= placebo, 2 = 5,000 units botox B, 3 = 10,000 units botox B) 47 | - age (in years) 48 | - sex (1 = F, 2 = M) 49 | - twstrs (total score of Toronto Western Spasmodic Torticollis Rating Scale, range 0-87) 50 | 51 | #### Citation 52 | 53 | A. Brashear, M.F. Lew, D.D. Dykstra, C.L. Comella, S.A. Factor, R.L. Rodnitzky, R. Trosch, C. Singer, M.F. Brin, J.J. Murray, J.D. Wallace, A. Willmer–Hulme, M. Koller (1999), Safety and efficacy of NeuroBloc (botulinum toxin type B) in type A–responsive cervical dystonia. Neurology, 53(7), 1439. 54 | 55 | -------------------------------------------------------------------------------- /man/description_docs/covid_desc.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "COVID-19 Testing Description Document" 3 | author: "Peter Higgins" 4 | date: "5/15/2021" 5 | output: 6 | html_document: 7 | theme: cerulean 8 | --- 9 | 10 | ```{r setup, include=FALSE} 11 | knitr::opts_chunk$set(echo = TRUE) 12 | ``` 13 | 14 | ### Source: 15 | This data set is from Amrom E. Obstfeld, who de-identified data on COVID-19 testing during the year 2020 at the Children's Hospital of Pennsylvania (aka CHOP). 16 | 17 | This data set contains data concerning testing for SARS-CoV2 via PCR as well as associated metadata. The data has been anonymized, time-shifted, and permuted. 18 | 19 | ### Abstract 20 | 21 | This data set contains data on SARS-CoV2 testing at a single hospital on 22 | 23 | ### Background 24 | 25 | The COVID-19 pandemic in 2020 required rapid development and deployment of a novel PCR test, and application to many outpatients with symptoms, and all inpatients upon admission to the emergency room or the hospital. 26 | These anonymized data (with fake subject_ids, names, ages, and genders) provide a snapshot of the first ~100 days of testing at a single pediatric hospital, with over 15,000 tests performed on patients in a variety of settings. The vast majority were a single type of test. These tests occurred in 88 named clinics (clinic_name) and in 5 demographic groups (demo_group). Roughly half of the tests were performed at drive-through sites (drive_thru_ind). 27 | 28 | The test result (result) can be either negative, positive, or invalid. 29 | The ct_result provides the cycle number at which the positive threshold was reached during PCR. Lower integer values for ct_result occur with a higher viral load (higher number of viral particles) in the test sample (deep nasal swab). 30 | 31 | The test order can have been placed as a one-off order, or within an order set. 32 | The payor_group for an ordered test can be one of 7 categories (includes insurance type and self-pay). 33 | The patients are categorized into 9 patient classes, including inpatient, emergency, outpatient, etc. 34 | The col_rec_tat variable records the time elapsed (in hours) between sample collection and receipt in the lab. 35 | The rec__ver_tat variable records the time elapsed (in hours) between sample receipt in the lab and result verification/posting. 36 | 37 | ### Study Design 38 | 39 | Prospective Cross-Sectional Cohort 40 | 41 | ### Subjects & Variables 42 | 43 | 15,524 participants were tested, from day 4 to day 107 of the COVID-19 pandemic. 44 | 45 | N = 15,524 subjects 46 | 17 variables 47 | 48 | ### Potential Data Analysis 49 | There is a dichotomous outcome variable (result) and a continuous outcome variable (ct_result). 50 | 51 | You can also look at the specimen processing time to receipt, and from receipt to verification as an outcome. Did processing times improve over the first 100 days? 52 | 53 | You can look at trends over time by pan_day. 54 | 55 | There are a lot of categorical variables that could be associated with the outcomes, including patient_class, clinic_name, drive_thru_ind, demo_group, payor_group, gender, and orderset. 56 | 57 | These categorical variables could also be interesting to explore with correlations and mosaic plots. -------------------------------------------------------------------------------- /man/description_docs/esoph_ca_desc.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Esophageal Cancer Dataset Description Document" 3 | author: "Peter Higgins" 4 | date: "8/10/2021" 5 | output: 6 | html_document: 7 | theme: cerulean 8 | --- 9 | 10 | ```{r setup, include=FALSE} 11 | knitr::opts_chunk$set(echo = TRUE) 12 | ``` 13 | 14 | 15 | 16 | ### Source: 17 | This data set is from a a statistical textbook, Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. IARC Lyon / Oxford University Press. Originally in base R datasets. 18 | 19 | This dataset is derived from papers from authors in France. 20 | 21 | ### Abstract 22 | 23 | Data from a case-control study of esophageal cancer in Ille-et-Vilaine, France, evaluating the effects of smoking and alcohol on the incidence of esophageal cancer. 200 cases and 975 controls were evaluated for alcohol and tobacco consumption. It is not entirely clear which publication was the source of this dataset. 24 | 25 | #### 1975 Abstract from AJ Tuyns, et al. 26 | Mortality from cancer of the oesophagus is known to be high in Brittany and Normandy. A morbidity survey was initiated to confirm this finding. A registry was set up in Rennes for the département of Ille-et-Vilaine. In the course of six years (1968-73) 718 cases were entered, 669 males and 49 females; the annual incidence rate for males is 29-4 per 100,000, one of the highest ever described. Most tumors are in the middle third of the oesophagus (49 per cent); 33 per cent are in the lower third. Most tumors were squamous cell carcinomas; 7 per cent were adenocarcinomas. There is an area of high endemicity in the rural areas of the north of Ille-et-Vilaine with rates reaching 60 per 100,000. The relationship with drinking habits is currently being investigated. 27 | [link](https://pubmed.ncbi.nlm.nih.gov/1116895/) 28 | 29 | #### 1977 Abstract from AJ Tuyns, et al. 30 | A retrospective case-control study of 200 male cases of oesophageal cancer and 778 population controls has been carried out in Ille-et-Vilanine (France). The logarithms of the relative risks of developing the disease increase linearly with daily consumption of alcohol and tobacco independently. The combined effect of both fit with a multiplicative model which is proposed. This model could be applicable to other situations. It explains the sex ratio and the urban/rural differences observed in Ille-er-Vilaine. The practical implications for public health purposes are briefly discussed. [link](https://pubmed.ncbi.nlm.nih.gov/861389/) 31 | 32 | ### Background 33 | 34 | Smoking and alcohol are associated risk factors for squamous cell cancer of the esophagus, rather than adenocarcinoma of the esophagus, which is associated with obesity and esophageal reflux. The majority (93% of the cases) in this study were of the squamous type, and more than 90% of cases occurred in males in this study in Brittany, France. The authors speculate that there may be an interaction between the effects of alcohol and tobacco. 35 | 36 | ### Study Design 37 | 38 | Retrospective case-control study of exposures (smoking, alcohol) and outcome (esophageal cancer) 39 | 40 | ### Subjects & Variables 41 | 42 | 200 cases and 975 controls were evaluated for consumption of alcohol and tobacco. 43 | 44 | 5 variables 45 | 46 | - agegp 47 | - alcgp 48 | - tpbgp 49 | - ncases 50 | - ncontrols 51 | 52 | #### Citations 53 | "Cancer of the oesophagus in Brittany: an incidence study in Ille-et-Vilaine", _Int J Epidmiol_, 1975, Volume 4, pages 55-59. 54 | by AJ Tuyns, G Masse. 55 | 56 | "Esophageal cancer in Ille-et-Vilaine in relation to levels of alcohol and tobacco consumption. Risks are multiplying", _Bulletin of Cancer_, 1977, volume 64, pages 45-60, by AJ Tuyns, G Pequignot, and OM Jensen. 57 | 58 | -------------------------------------------------------------------------------- /man/description_docs/indometh_desc.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Indomethacin Pharmacokinetics Dataset" 3 | author: "Peter Higgins" 4 | date: "8/10/2021" 5 | output: 6 | html_document: 7 | theme: cerulean 8 | --- 9 | 10 | ```{r setup, include=FALSE} 11 | knitr::opts_chunk$set(echo = TRUE) 12 | ``` 13 | 14 | 15 | ### Source: 16 | This data set is from a pharmacokinetic study of intravenous dosing of the anti-inflammatory and pain relieving medication, indomethacin, in 6 subjects, published in 1976 in the Journal of Pharmacokinetics and Biopharmaceutics. 17 | 18 | Kwan, Breault, Umbenhauer, McMahon and Duggan (1976) Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Pharmacokinetics and Biopharmaceutics. 1976 Jun;4(3):255-80. doi: 10.1007/BF01063617. 19 | 20 | This dataset is is one of the core datasets in base R. It can be used for examples of line plots over time, grouped by subject, and non-linear modeling, including general additive models (GAMs). 21 | 22 | ### Abstract 23 | 24 | This data set contains data on 6 healthy volunteer subjects who participated in a pharmacokinetic study of intravenous indomethacin. Indomethacin is an anti-inflammatory and pain-relieving non-steroidal medication. It can be administered by the intravenous, oral, or rectal suppository routes. Some of the indomethacin is excreted in the bile and reabsorbed by the intestine. This phenomenon, called enterohepatic circulation, keeps the drug around longer than would be expected otherwise. Each subject in Study 1 (intravenous route) received a single 50 mg dose of radioactively labeled indomethacin (^14^-carbon-labeled, with each dose containing 25 microCuries of radioactivity). Subjects received a standard meal (one 8-oz can of Metrecal, 8 oz of whole milk, and one medium-size apple) 30 rain prior to medication and 8 oz of water every 2 hr throughout the waking hours to ensure adequate urine output. Blood samples were taken at frequent intervals over the first 8 hours after dosing, and the quantity of indomethacin in the plasma (as well as stool and urine) at each time point was measured in micrograms per milliliter. This data set only contains the plasma measurements from Table 1 on page 258 of the manuscript. While this paper was published in 1976 (post-Tuskegee reveal), there is no mention of ethics review, IRB review, or consent of the healthy volunteers. 25 |
26 |
27 | The abstract from the original manuscript: 28 | 29 | There are no discernible quantitative differences in the biotransformation and the excretion of indomethacin following oral, rectal, and intravenous administration of indomethacin-2-^14^C. Approximately 50% (range 24-115% for n = 6) of an intravenous dose undergoes enterohepatic circulation. Thus the bioavailability of indomethacin to the systemic circulation may exceed the administered dose. Relative to the intravenous dose, indomethacin is 80 and 100% bioavailable from suppositories and capsules, respectively. Absorption and/or reabsorption appears to be more rapid and uniform by the rectal route. Recognition of the attributes of biliary recycling also helps to explain the observed variability in apparent plasma half-life, while their neglect requires alternative explanationsfor anomalies between the disappearance rate from plasma and the corresponding appearance rate in urine. 30 | 31 | ### Study Design 32 | 33 | Cohort Pharmacokinetic study over 8 hours. 34 | 35 | ### Subjects & Variables 36 | 37 | 6 participants were administered radioactive indomethacin, and plasma levels were measured at multiple time points over 8 hours. 38 | 39 | ### Variables 40 | 41 | - Subject 42 | - time 43 | - concentration 44 | 45 | #### Citation(s) 46 | 47 | Kwan, Breault, Umbenhauer, McMahon and Duggan (1976) Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Pharmacokinetics and Biopharmaceutics. 1976 Jun;4(3):255-80. doi: 10.1007/BF01063617. 48 | 49 | -------------------------------------------------------------------------------- /man/description_docs/polyps_desc.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Sulindac v. Polyps RCT Dataset Description Document" 3 | author: "Peter Higgins" 4 | date: "8/10/2021" 5 | output: 6 | html_document: 7 | theme: cerulean 8 | --- 9 | 10 | ```{r setup, include=FALSE} 11 | knitr::opts_chunk$set(echo = TRUE) 12 | ``` 13 | 14 | ## Source: 15 | 16 | Source: This data set is from a study published in 1993 in the New England Journal of Medicine, 17 | 18 | F. M. Giardiello, S. R. Hamilton, A. J. Krush, S. Piantadosi, L. M. Hylind, P. Celano, S. V. Booker, C. R. Robinson and G. J. A. Offerhaus (1993), Treatment of colonic and rectal adenomas with sulindac in familial adenomatous polyposis. New England Journal of Medicine, 328(18), 1313–1316. 19 | 20 | This dataset is derived from and improved upon from the {HSAUR} package. 21 | 22 | ### Abstract 23 | 24 | A randomized, controlled trial of sulindac to reduce colon polyp formation in patients with familial adenomatous polyposis. 25 | 26 | ### Background 27 | 28 | FAP is an inherited condition caused by mutations in the APC (Adenomatous Polyposis Coli) gene that leads to early and frequent formation of precancerous polyps of the colon at a young age, and invariably leads to the development of colon cancer at a young age. 29 | Early, frequent surveillance colonoscopy and polyp removal is helpful, but this study examined whether there is a beneficial effect of preventive medical therapy with the nonsteroidal pain reliever, sulindac, versus placebo in a RCT vs placebo in 22 participants, with polyp number measured (via colonoscopy) at baseline, 3 months, and 12 months after starting the study drug. 30 | 31 | ### Study Design 32 | 33 | Prospective randomized controlled trial of sulindac vs. placebo in the prevention of colonic polyps in FAP (Familial Adenomatous Polyposis). 34 | 35 | ### Subjects & Variables 36 | 37 | 22 participants were assigned to 1 of 2 treatments, sulindac or placebo. 38 | 39 | The initial sample consisted of 22 people with known FAP. 40 | 41 | N = 22 subjects (2 dropped out between month 3 and 12) 42 | 7 variables 43 | 44 | - participant_id 45 | - sex 46 | - age 47 | - baseline (number of polyps at baseline visit) 48 | - treatment (sulindac or placebo) 49 | - number3m (number of polyps at 3 months) 50 | - number6m (number of polyps at 6 months) 51 | 52 | #### Citation 53 | 54 | F. M. Giardiello, S. R. Hamilton, A. J. Krush, S. Piantadosi, L. M. Hylind, P. Celano, S. V. Booker, C. R. Robinson and G. J. A. Offerhaus (1993), Treatment of colonic and rectal adenomas with sulindac in familial adenomatous polyposis. New England Journal of Medicine, 328(18), 1313–1316. 55 | -------------------------------------------------------------------------------- /man/description_docs/theoph_desc.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Theophylline Pharmacokinetics Dataset" 3 | author: "Peter Higgins" 4 | date: "8/10/2021" 5 | output: 6 | html_document: 7 | theme: cerulean 8 | --- 9 | 10 | ```{r setup, include=FALSE} 11 | knitr::opts_chunk$set(echo = TRUE) 12 | ``` 13 | 14 | 15 | ### Source: 16 | This data set is from a pharmacokinetic study of oral dosing of the anti-asthma medication, theophylline, in 12 subjects over 25 hours, published By Dr. Robert A. Upton, PhD around 1980. The original publication, if any, is unclear and not cited. These data were used in a package named `nlme`, and reported in Boeckmann, A.J., et al. The original publication on theophylline, if any, is unclear and not cited by the package developers. Dr. Upton was director of the Drug Studies Unit at the University of California, San Francisco, and did publish several papers on theophylline pharmacokinetics around 1980-1984, and these data could have been from one of these studies. 17 | 18 | Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994), NONMEM Users Guide: Part V, NONMEM Project Group, University of California, San Francisco. 19 | 20 | Davidian, M. and Giltinan, D. M. (1995) Nonlinear Models for Repeated Measurement Data, Chapman & Hall (section 5.5, p. 145 and section 6.6, p. 176) 21 | 22 | Pinheiro, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-PLUS, Springer (Appendix A.29) 23 | 24 | This dataset is in the datasets package in base R. It can be used for examples of line plots over time, grouped by subject, and non-linear modeling, including general additive models (GAMs). 25 | 26 | ### Abstract 27 | 28 | This data set contains data on 12 healthy volunteer subjects who participated in a pharmacokinetic study of oral Theophylline. Theophylline is an methylxanthine anti-asthma medication, which acts as a bronchodilator, with secondary effects to strengthen diaphragm contraction, reduce pulmonary artery pressures, and reduce mast cell release. It can be administered by the intravenous, oral, or rectal suppository routes. Unfortunately, the plasma level in blood varies considerably between patients, because of differences in drug clearance, which is affected by body mass, age, smoking, liver and heart function, and viral infections. To complicate this drug further, it has important interactions with a number of other common medicines which can increase or decrease the drug level. Each subject in this study received a single oral dose of 300 mg of theophylline, which has been converted to a milligrams per kilogram dose. Blood samples were taken at frequent intervals over the next 25 hours after dosing, and the quantity of theophylline in the plasma at each time point was measured in micrograms per milliliter of plasma. 29 |
30 | 31 | ### Study Design 32 | 33 | Cohort Pharmacokinetic study over 25 hours. 34 | 35 | ### Subjects & Variables 36 | 37 | 12 participants were administered oral theophylline, and plasma levels were measured at multiple time points over 25 hours. 38 | 39 | ### Variables 40 | 41 | - Subject 42 | - Wt (kg) 43 | - Dose (mg/kg) 44 | - Time (hours) 45 | - Concentration (mcg/mL) 46 | 47 | #### Citation(s) 48 | 49 | Kwan, Breault, Umbenhauer, McMahon and Duggan (1976) Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Pharmacokinetics and Biopharmaceutics. 1976 Jun;4(3):255-80. doi: 10.1007/BF01063617. 50 | 51 | -------------------------------------------------------------------------------- /man/esoph_ca.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/esoph_ca.R 3 | \docType{data} 4 | \name{esoph_ca} 5 | \alias{esoph_ca} 6 | \title{esoph_ca: Esophageal Cancer dataset} 7 | \format{ 8 | A data frame with 88 rows and 5 variables, with 200 cases and 975 controls. 9 | 10 | \describe{ 11 | \item{agegp}{6 levels of age: "25-34", "35-44", "45-54", "55-64", "65-74", "75+"; type: ordinal factor} 12 | \item{alcgp}{4 levels of alcohol consumption: "0-39g/day", "40-79", "80-119", "120+"; type: ordinal factor} 13 | \item{tobgp}{4 levels of tobacco consumption: "0-9g/day", "10-19", '20-29", "30+"; type: ordinal factor} 14 | \item{ncases}{Number of cases; type: integer} 15 | \item{ncontrols}{Number of controls; type: integer} 16 | } 17 | } 18 | \source{ 19 | Breslow, N. E. and Day, N. E. (1980) Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies. IARC Lyon / Oxford University Press. 20 | Originally in base R datasets. 21 | } 22 | \usage{ 23 | esoph_ca 24 | } 25 | \description{ 26 | Data from a case-control study of esophageal cancer in Ille-et-Vilaine, France, evaluating the effects of smoking and alcohol on the incidence of esophageal cancer. Smoking and alcohol are associated risk factors for squamous cell cancer of the esophagus, rather than adenocarcinoma of the esophagus, which is associated with obesity and esophageal reflux (more details available below the variable definitions). 27 | } 28 | \details{ 29 | An original base R dataset, though of somewhat unclear origin. The statistical textbook source is clear, though it is not clear which of the original epidemiological papers on esophageal cancer in Ille-et-Vilaine is referred to by this dataset. The original authors of the medical study were \strong{not} credited in the base R dataset. There are several possible papers in PubMed, none of which quite match up with this dataset. This could be from Tuyns, AJ, et al., Bull Cancer, 1977;64(1):45-60, but this paper reports 778 controls, rather than the 975 found here. A 1975 paper from the same group reported 718 cases (Int J Epidemiol, 1975 Mar;4(1):55-9. doi: 10.1093/ije/4.1.55.). There is also another possible source - a 1975 paper from the same group, \emph{Usefulness of population controls in retrospective studies of alcohol consumption. Experience from a case--control study of esophageal cancer in Ille-et-Vilaine, France}, Journal of Studies on Alcohol, 39(1): 175-182 (1978), which is behind a publisher paywall. 30 | } 31 | \section{Figure 1 Benign FNA of Breast}{ 32 | Benign fine needle aspirate (FNA) of a breast lesion. Notice the regular size of cells and nuclei, which are organized in orderly spacing. The nuclei are homogeneously dark with few visible nucleoli. 33 | \if{html}{\figure{benign_breast.png}{options: width=100\%}} 34 | } 35 | 36 | \section{Figure 2 Cancerous FNA of Breast}{ 37 | Malignant (cancerous) fine needle aspirate (FNA) of a breast lesion. Notice the very irregular size of cells and nuclei, which are are disorganized and seem to be growing over each other. The nuclei are also less homogeneously dark and more granular, suggesting active transcription from the dark nucleoli within each nucleus. 38 | \if{html}{\figure{malignant_breast.png}{options: width=100\%}} 39 | } 40 | 41 | \keyword{datasets} 42 | -------------------------------------------------------------------------------- /man/figures/benign_breast.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/man/figures/benign_breast.png -------------------------------------------------------------------------------- /man/figures/hex-medicaldata.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/man/figures/hex-medicaldata.png -------------------------------------------------------------------------------- /man/figures/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/man/figures/logo.png -------------------------------------------------------------------------------- /man/figures/malignant_breast.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/man/figures/malignant_breast.png -------------------------------------------------------------------------------- /man/indometh.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/Indometh.R 3 | \docType{data} 4 | \name{indometh} 5 | \alias{indometh} 6 | \title{Cohort Study of the Pharmacokinetics of Intravenous Indomethacin} 7 | \format{ 8 | A data frame with 66 observations and 3 variables 9 | \describe{ 10 | \item{Subject}{subject id number for each participant; type: character} 11 | \item{time}{Time from initial dose in hours; type: double} 12 | \item{conc}{Concentration of indomethacin in the plasma in micrograms per milliliter' type: double} 13 | } 14 | } 15 | \source{ 16 | Kwan, Breault, Umbenhauer, McMahon and Duggan (1976) Kinetics of Indomethacin absorption, elimination, and enterohepatic circulation in man. Pharmacokinetics and Biopharmaceutics. 1976 Jun;4(3):255-80. doi: 10.1007/BF01063617. 17 | } 18 | \usage{ 19 | indometh 20 | } 21 | \description{ 22 | Results of a Cohort Study of the Pharmacokinetics of Intravenous Indomethacin, with plasma concentrations over time (\strong{more details} available below the variable definitions). \cr 23 | } 24 | \details{ 25 | This data set contains data on 6 healthy volunteer subjects who participated in a pharmacokinetic study of intravenous indomethacin. Indomethacin is an anti-inflammatory and pain-relieving non-steroidal medication. It can be administered by the intravenous, oral, or rectal suppository routes. Some of the indomethacin is excreted in the bile and reabsorbed by the intestine. This phenomenon, called enterohepatic circulation, keeps the drug around longer than would be expected otherwise. \cr 26 | Each subject in Study 1 (intravenous route) received a single 50 mg dose of radioactively labeled indomethacin (^14^-carbon-labeled, with each dose containing 25 microCuries of radioactivity). Subjects received a standard meal (one 8-oz can of Metrecal, 8 oz of whole milk, and one medium-size apple) 30 rain prior to medication and 8 oz of water every 2 hr throughout the waking hours to ensure adequate urine output.\cr 27 | Blood samples were taken at frequent intervals over the first 8 hours after dosing, and the quantity of indomethacin in the plasma (as well as stool and urine) at each time point was measured in micrograms per milliliter. This data set only contains the plasma measurements from Table 1 on page 258 of the manuscript. While this paper was published in 1976 (post-Tuskegee reveal), there is no mention of ethics review, IRB review, or consent of the healthy volunteers. \cr 28 | The abstract from the original manuscript: \cr 29 | There are no discernible quantitative differences in the biotransformation and the excretion of indomethacin following oral, rectal, and intravenous administration of indomethacin-2-^14^C. Approximately 50\% (range 24-115\% for n = 6) of an intravenous dose undergoes enterohepatic circulation. Thus the bioavailability of indomethacin to the systemic circulation may exceed the administered dose. Relative to the intravenous dose, indomethacin is 80 and 100\% bioavailable from suppositories and capsules, respectively. Absorption and/or reabsorption appears to be more rapid and uniform by the rectal route. Recognition of the attributes of biliary recycling also helps to explain the observed variability in apparent plasma half-life, while their neglect requires alternative explanations for anomalies between the disappearance rate from plasma and the corresponding appearance rate in urine.\cr 30 | } 31 | \keyword{datasets} 32 | -------------------------------------------------------------------------------- /man/polyps.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/polyps.R 3 | \docType{data} 4 | \name{polyps} 5 | \alias{polyps} 6 | \title{RCT of Sulindac for Polyp Prevention in Familial Adenomatous Polyposis} 7 | \format{ 8 | A data frame with 22 observations and 7 variables 9 | \describe{ 10 | \item{participant_id}{id number for each participant; type: character} 11 | \item{sex}{participant sex, levels: female, male; type: factor} 12 | \item{age}{age in years; type: numeric} 13 | \item{baseline}{number of colonic polyps at baseline; type: numeric} 14 | \item{treatment}{treatment assignment, levels: sulindac, placebo; type: factor} 15 | \item{number3m}{number of colonic polyps at 3 months; type: numeric} 16 | \item{number12m}{number of colonic polyps at 12 months; type: numeric} 17 | } 18 | } 19 | \source{ 20 | This data set is from a study published in 1993 in the New England Journal of Medicine, 21 | 22 | F. M. Giardiello, S. R. Hamilton, A. J. Krush, S. Piantadosi, L. M. Hylind, P. Celano, S. V. Booker, C. R. Robinson and G. J. A. Offerhaus (1993), Treatment of colonic and rectal adenomas with sulindac in familial adenomatous polyposis. New England Journal of Medicine, 328(18), 1313-1316. 23 | 24 | This dataset is derived from and improved upon from the {HSAUR} package. 25 | } 26 | \usage{ 27 | polyps 28 | } 29 | \description{ 30 | Results of a randomized, placebo-controlled trial of sulindac in the reduction of colonic polyps in Familial Adenomatous Polyposis (FAP) (\strong{more details} available below the variable definitions). \cr 31 | } 32 | \details{ 33 | FAP is an inherited condition caused by mutations in the APC (Adenomatous Polyposis Coli) gene that leads to early and frequent formation of precancerous polyps of the colon at a young age, and invariably leads to the development of colon cancer at a young age. \cr 34 | Early, frequent surveillance colonoscopy and polyp removal is helpful, but this study examined whether there is a beneficial effect of preventive medical therapy with the nonsteroidal pain reliever, sulindac, versus placebo in a RCT vs placebo in 22 participants, with polyp number measured (via colonoscopy) at baseline, 3 months, and 12 months after starting the study drug. Note that one subject did not return for the 12 month colonoscopy. 35 | } 36 | \keyword{datasets} 37 | -------------------------------------------------------------------------------- /man/theoph.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/theoph.R 3 | \docType{data} 4 | \name{theoph} 5 | \alias{theoph} 6 | \title{Cohort Study of the Pharmacokinetics of Oral Theophylline} 7 | \format{ 8 | A data frame with 132 observations and 5 variables 9 | \describe{ 10 | \item{Subject}{subject id number for each participant; type: ordinal factor} 11 | \item{Wt}{Weight in kilograms; type: double} 12 | \item{Dose}{Dose in milligrams per kilogram; type: double} 13 | \item{Time}{Time from initial dose in hours; type: double} 14 | \item{conc}{Concentration of theophylline in the plasma in micrograms per milliliter' type: double} 15 | } 16 | } 17 | \source{ 18 | Boeckmann, A. J., Sheiner, L. B. and Beal, S. L. (1994), NONMEM Users Guide: Part V, NONMEM Project Group, University of California, San Francisco. 19 | Note that the original data collector, Robert A. Upton, is not credited, nor is the original work cited. 20 | } 21 | \usage{ 22 | theoph 23 | } 24 | \description{ 25 | Results of a Cohort Study of the Pharmacokinetics of Oral Theophylline, with plasma concentrations over time (\strong{more details} available below the variable definitions). \cr 26 | } 27 | \details{ 28 | This data set is from a pharmacokinetic study of oral dosing of the anti-asthma medication, theophylline, in 12 subjects over 25 hours, published By Dr. Robert A. Upton around 1980. The original publication, if any, is unclear and not cited. These data were used in a package named \code{nlme}, and reported in Boeckmann, A.J., et al.Dr. Upton did publish several papers on theophylline pharmacokinetics around 1980-1984, and these data could have been from one of these.\cr 29 | Theophylline is an methylxanthine anti-asthma medication, which acts as a bronchodilator, with secondary effects to strengthen diaphragm contraction, reduce pulmonary artery pressures, and reduce mast cell release. It can be administered by the intravenous, oral, or rectal suppository routes.\cr 30 | Each subject in this Study (oral route) received a single oral dose of theophylline. 31 | Blood samples were taken at frequent intervals over the first 25 hours after dosing, and the quantity of theophylline in the plasma at each time point was measured in micrograms per milliliter. \cr 32 | Unfortunately, the theophylline plasma level in blood varies considerably between patients, because of differences in drug clearance, which is affected by body mass, age, smoking, liver and heart function, and viral infections. To complicate this drug further, it has important interactions with a number of other common medicines which can increase or decrease the drug level. Each subject in this study received a single oral dose of 300 mg of theophylline, which has been converted to a milligrams per kilogram dose. Blood samples were taken at frequent intervals over the next 25 hours after dosing, and the quantity of theophylline in the plasma at each time point was measured in micrograms per milliliter of plasma. \cr 33 | } 34 | \keyword{datasets} 35 | -------------------------------------------------------------------------------- /medicaldata.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Default 4 | SaveWorkspace: Default 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: Sweave 13 | LaTeX: pdfLaTeX 14 | 15 | StripTrailingWhitespace: Yes 16 | 17 | BuildType: Package 18 | PackageUseDevtools: Yes 19 | PackageInstallArgs: --no-multiarch --with-keep.source 20 | -------------------------------------------------------------------------------- /pkgdown/favicon/apple-touch-icon-120x120.png: 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https://raw.githubusercontent.com/higgi13425/medicaldata/a73901fefd7cf05b832c9a73663c066a5ac22f96/pkgdown/favicon/favicon.ico -------------------------------------------------------------------------------- /vignettes/.gitignore: -------------------------------------------------------------------------------- 1 | *.html 2 | *.R 3 | -------------------------------------------------------------------------------- /vignettes/Introduction.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: Introduction 3 | output: rmarkdown::html_vignette 4 | vignette: > 5 | %\VignetteIndexEntry{Introduction} 6 | %\VignetteEngine{knitr::rmarkdown} 7 | %\VignetteEncoding{UTF-8} 8 | --- 9 | 10 | ```{r setup, include = FALSE} 11 | knitr::opts_chunk$set( 12 | collapse = TRUE, 13 | comment = "#>" 14 | ) 15 | ``` 16 | 17 | The {medicaldata} package is purely a data package, for the purpose of collecting well-documented medical datasets in one package for teaching to medical students, residents, fellows, nurses, pharmacists, physician assistants, and anyone else who wants to learn R for use with medical data. 18 | 19 | The de-identified datasets included in this package come from a variety of sources, including: 20 | 21 | - donations by generous authors 22 | - sourcing from other R packages 23 | - the TSHS (Teaching Statistics in the Health Sciences) project, which can be found at [link](https://www.causeweb.org/tshs/), and is sponsored by the American Statistical Association. 24 | - reconstruction of datasets from historically important clinical studies, like the scurvy study by James Lind, and the first clinical trial of Streptomycin in Pulmonary Tuberculosis. 25 | 26 | Each dataset is documented in three ways: 27 | 28 | - a help file, which can be accessed with `help('dataset_name')` from the Console pane 29 | - a pdf document describing the study, found at the Github README page [here](https://github.com/higgi13425/medicaldata) 30 | - a pdf codebook, found at the Github README page [here](https://github.com/higgi13425/medicaldata) 31 | 32 | Currently, the datasets included are: 33 | 34 | - Streptomycin in Tuberculosis (`strep_tb`) 35 | - Scurvy RCT (`scurvy`) 36 | - Indomethacin RCT to Prevent Post-ERCP Pancreatitis (`indo_rct`) 37 | - Sulindac RCT for polyps (`polyps`) 38 | - Covid Testing (`covid_testing`) 39 | - Blood Storage (prostate cancer) (`blood_storage`) 40 | - Cytomegalovirus in BMT (`cytomegalovirus`) 41 | - Esophageal cancer (`esoph_ca`) 42 | - Laryngoscope (`laryngoscope`) 43 | - Licorice gargle (`licorice_gargle`) 44 | - Obstetric Periodontal Therapy (`opt`) 45 | - Smartpill (`smartpill`) 46 | - Supraclavicular (`supraclavicular`) 47 | - Indomethacin Pharmacokinetics (`indometh`) 48 | - Theophylline Pharmacokinetics (`theoph`) 49 | - Diabetes Development in Pima Indians (`diabetes`) 50 | - Thiopurine Effectiveness in IBD (`thiomon`) 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