`, etc.) that has more than one element. Defaults to 1 (for ``).
76 | getTopLevel: function($scope) {
77 | for (var i = 1; i <= 6; i++) {
78 | var $headings = this.findOrFilter($scope, 'h' + i);
79 | if ($headings.length > 1) {
80 | return i;
81 | }
82 | }
83 |
84 | return 1;
85 | },
86 |
87 | // returns the elements for the top level, and the next below it
88 | getHeadings: function($scope, topLevel) {
89 | var topSelector = 'h' + topLevel;
90 |
91 | var secondaryLevel = topLevel + 1;
92 | var secondarySelector = 'h' + secondaryLevel;
93 |
94 | return this.findOrFilter($scope, topSelector + ',' + secondarySelector);
95 | },
96 |
97 | getNavLevel: function(el) {
98 | return parseInt(el.tagName.charAt(1), 10);
99 | },
100 |
101 | populateNav: function($topContext, topLevel, $headings) {
102 | var $context = $topContext;
103 | var $prevNav;
104 |
105 | var helpers = this;
106 | $headings.each(function(i, el) {
107 | var $newNav = helpers.generateNavItem(el);
108 | var navLevel = helpers.getNavLevel(el);
109 |
110 | // determine the proper $context
111 | if (navLevel === topLevel) {
112 | // use top level
113 | $context = $topContext;
114 | } else if ($prevNav && $context === $topContext) {
115 | // create a new level of the tree and switch to it
116 | $context = helpers.createChildNavList($prevNav);
117 | } // else use the current $context
118 |
119 | $context.append($newNav);
120 |
121 | $prevNav = $newNav;
122 | });
123 | },
124 |
125 | parseOps: function(arg) {
126 | var opts;
127 | if (arg.jquery) {
128 | opts = {
129 | $nav: arg
130 | };
131 | } else {
132 | opts = arg;
133 | }
134 | opts.$scope = opts.$scope || $(document.body);
135 | return opts;
136 | }
137 | },
138 |
139 | // accepts a jQuery object, or an options object
140 | init: function(opts) {
141 | opts = this.helpers.parseOps(opts);
142 |
143 | // ensure that the data attribute is in place for styling
144 | opts.$nav.attr('data-toggle', 'toc');
145 |
146 | var $topContext = this.helpers.createChildNavList(opts.$nav);
147 | var topLevel = this.helpers.getTopLevel(opts.$scope);
148 | var $headings = this.helpers.getHeadings(opts.$scope, topLevel);
149 | this.helpers.populateNav($topContext, topLevel, $headings);
150 | }
151 | };
152 |
153 | $(function() {
154 | $('nav[data-toggle="toc"]').each(function(i, el) {
155 | var $nav = $(el);
156 | Toc.init($nav);
157 | });
158 | });
159 | })();
160 |
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1 |
2 | Articles • condsurv
6 |
7 |
8 |
9 |
56 |
57 |
58 |
59 |
60 |
61 |
62 | Articles
63 |
64 |
65 |
66 | All vignettes
67 |
68 |
69 | - Estimate conditional survival
70 | -
71 |
- Plot conditional survival curves
72 | -
73 |
74 |
75 |
76 |
77 |
78 |
87 |
88 |
89 |
90 |
91 |
92 |
93 |
94 |
95 |
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8 | Page not found (404) • condsurv
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90 |
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/archive/condKMapp.R:
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1 | #' Interactive conditional survival Shiny app
2 | #'
3 | #' \code{condKMapp} produces an interactive Shiny app containing output from
4 | #' both the \code{condKMest} function and the \code{condKMplot} function.
5 | #'
6 | #' @param .basekm \code{survfit} object
7 | #'
8 | #' @return The output includes 1) a plot with one overall Kaplan-Meier survival
9 | #' curve and an additional Kaplan-Meier survival curve for each time on which
10 | #' you are conditioning and 2) a table with conditional survival estimates at
11 | #' the specified survival time along with their associated confidence intervals,
12 | #' with one row for each time on which you are conditioning.
13 | #'
14 | #' @export
15 | #'
16 |
17 | condKMapp <- function(.basekm) {
18 | library(shiny)
19 | library(survival)
20 |
21 | shinyApp(
22 | ui = fluidPage(
23 | pageWithSidebar(
24 | headerPanel("Conditional survival estimates"),
25 | sidebarPanel(
26 | helpText("This interactive interface allows you to adjust the
27 | survival time of interest (using the numeric entry box below)
28 | and to select a single survival time, or range of survival times, on
29 | which to condition (using the slider bar below)."),
30 | numericInput(
31 | inputId = "survtime", label = "Enter the survival time of interest (in years)",
32 | value = 5, min = 0, max = 10
33 | ),
34 | sliderInput(
35 | inputId = "condtime", label = "Select the range of times on which to condition (in years)",
36 | value = c(1, 4), min = 1, max = 10, step = 1
37 | ),
38 | helpText("The output includes 1) a plot with one overall Kaplan-Meier
39 | survival curve and an additional Kaplan-Meier survival curve for
40 | each time on which you are conditioning and 2) a table
41 | with conditional survival estimates at the specified
42 | survival time along with their associated confidence intervals,
43 | with one row for each time on which you are conditioning.")
44 | ),
45 | mainPanel(
46 | plotOutput(outputId = "condplot"),
47 | tableOutput(outputId = "condtab")
48 | )
49 | )
50 | ),
51 |
52 | server = function(input, output) {
53 | output$condplot <- renderPlot({
54 | if (class(.basekm) != "survfit") {
55 | stop("Argument to .basekm must be of class survfit")
56 | }
57 | if (max(input$condtime) > max(.basekm$time)) {
58 | stop(paste(
59 | "The range of times on which to condition specifies value(s) outside the range of observed times;",
60 | "the maximum observed time is", round(max(.basekm$time), 2)
61 | ))
62 | }
63 |
64 | plot(.basekm,
65 | conf.int = F, xlab = "Years", ylab = "Survival probability",
66 | lwd = 1, mark.time = F, main = "Conditional survival curves"
67 | )
68 | at <- seq(from = min(input$condtime), to = max(input$condtime), by = 1)
69 | nt <- length(at)
70 | fitkm <- list()
71 | for (i in 1:nt) {
72 | fitkm[[i]] <- survfit(
73 | formula = as.formula(.basekm$call$formula),
74 | data = eval(.basekm$call$data),
75 | start.time = at[i]
76 | )
77 | lines(fitkm[[i]], conf.int = F, col = i + 1, lwd = 1, mark.time = F)
78 | abline(v = i, lty = 3)
79 | }
80 | }, height = 400, width = 500)
81 |
82 | output$condtab <- renderTable({
83 | if (class(.basekm) != "survfit") {
84 | stop("Argument to .basekm must be of class survfit")
85 | }
86 | if (max(input$condtime) > max(.basekm$time)) {
87 | stop(paste(
88 | "The range of times on which to condition specifies value(s) outside the range of observed times;",
89 | "the maximum observed time is", round(max(.basekm$time), 2)
90 | ))
91 | }
92 | if (max(input$survtime) > max(.basekm$time)) {
93 | stop(paste(
94 | "The survival time of interest specifies a value outside the range of observed times;",
95 | "the maximum observed time is", round(max(.basekm$time), 2)
96 | ))
97 | }
98 |
99 | at <- seq(from = min(input$condtime), to = max(input$condtime), by = 1)
100 | nt <- length(at)
101 | cstab <- NULL
102 | for (i in 1:nt) {
103 | if (at[i] < input$survtime) {
104 | cs <- summary(.basekm, times = c(at[i], input$survtime))$surv[2] /
105 | summary(.basekm, times = c(at[i], input$survtime))$surv[1]
106 | cs.sq <- cs^2
107 | d <- .basekm$n.event[.basekm$time >= at[i] & .basekm$time <= input$survtime &
108 | .basekm$n.event > 0]
109 | r <- .basekm$n.risk[.basekm$time >= at[i] & .basekm$time <= input$survtime &
110 | .basekm$n.event > 0]
111 | dr <- d / (r * (r - d))
112 | var.cs <- cs.sq * sum(dr)
113 | ci <- cs + c(-1, 1) * (qnorm(0.975) * sqrt(var.cs))
114 | if (ci[1] < 0) {
115 | warning("Lower bound of CI has been truncated to 0")
116 | }
117 | if (ci[2] > 1) {
118 | warning("Upper bound of CI has been truncated to 1")
119 | }
120 | ci.cs <- round(ci, 2)
121 | if (ci.cs[1] < 0) {
122 | ci.cs[1] <- 0
123 | }
124 | if (ci.cs[2] > 1) {
125 | ci.cs[2] <- 1
126 | }
127 | cstab <- data.frame(rbind(cstab, c(round(at[i]), round(cs, 2), ci.cs[1], ci.cs[2])),
128 | stringsAsFactors = FALSE
129 | )
130 | }
131 | }
132 | colnames(cstab) <- c("Conditional time", "Conditional survival", "Lower 95% CI", "Upper 95% CI")
133 | cstab
134 | }, digits = c(0, 0, 2, 2, 2))
135 | }
136 | )
137 | }
138 |
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1 |
2 | Function reference • condsurv
6 |
7 |
8 |
9 |
56 |
57 |
58 |
59 |
60 |
61 |
62 | Reference
63 |
64 |
65 |
66 | All functions
67 |
68 |
69 |
70 |
71 |
72 | Estimate conditional survival with a 95% confidence interval
74 |
75 |
76 | Generate conditional survival plots using ggplot2
78 |
79 |
82 |
83 |
84 |
85 |
94 |
95 |
96 |
97 |
98 |
99 |
100 |
101 |
102 |
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1 |
2 | Authors and Citation • condsurv
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7 |
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113 |
114 |
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/docs/reference/conditional_surv_est.html:
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1 |
2 | Estimate conditional survival with a 95% confidence interval — conditional_surv_est • condsurv
7 |
8 |
9 |
10 |
57 |
58 |
59 |
60 |
61 |
62 |
63 | Estimate conditional survival with a 95% confidence interval
64 |
65 | conditional_surv_est.Rd
66 |
67 |
68 |
69 | conditional_surv_est estimates the Kaplan-Meier conditional survival at
70 | fixed time points and produces a 95% confidence interval
71 |
72 |
73 |
74 | conditional_surv_est(basekm, t1, t2)
75 |
76 |
77 |
78 | Arguments
79 | - basekm
80 | survfit object
- t1
84 | the time on which to condition
- t2
88 | the survival time to estimate
91 |
92 | Value
93 |
94 |
95 | A list where cs_est is the conditional survival estimate,
96 | cs_lci is the lower bound of the 95% confidence interval and
97 | cs_uci is the upper bound of the 95% confidence interval
98 |
99 |
100 | Details
101 | For example, if t1 = 2 and t2 = 5, the function
102 | will return the probability of surviving to year 5 conditioned on having
103 | already survived to year 2. See the vignette
104 | at http://www.emilyzabor.com/condsurv/articles/estimate_cs.html for
105 | details on calculations of conditional survival estimates and confidence
106 | intervals, and examples.
107 |
108 |
109 |
110 |
113 |
114 |
115 |
116 |
125 |
126 |
127 |
128 |
129 |
130 |
131 |
132 |
133 |
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1 |
2 | Generate conditional survival plots using ggplot2 — gg_conditional_surv • condsurv
7 |
8 |
9 |
10 |
57 |
58 |
59 |
60 |
61 |
62 |
63 | Generate conditional survival plots using ggplot2
64 |
65 | gg_conditional_surv.Rd
66 |
67 |
68 |
69 | gg_conditional_surv produces a Kaplan-Meier plot for a variety of times on
70 | which to condition using ggplot2
71 |
72 |
73 |
74 | gg_conditional_surv(
75 | basekm,
76 | at,
77 | main = NULL,
78 | xlab = "Years",
79 | ylab = "Survival probability",
80 | lwd = 1
81 | )
82 |
83 |
84 |
85 | Arguments
86 | - basekm
87 | survfit object
- at
91 | vector of times on which to condition
- main
95 | plot title
- xlab
99 | x-axis label
- ylab
103 | y-axis label, defaults to "Survival probability"
- lwd
107 | plot line width, defaults to 1
110 |
111 | Value
112 |
113 |
114 | A ggplot with a line for the overall Kaplan-Meier plot and one
115 | additional line for each value in at
116 |
117 |
118 |
119 | #' @details See the vignette
120 | at http://www.emilyzabor.com/condsurv/articles/plot_cs.html for
121 | details and examples.
122 |
123 |
124 |
125 |
128 |
129 |
130 |
131 |
140 |
141 |
142 |
143 |
144 |
145 |
146 |
147 |
148 |
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1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 | Conditional survival estimates and plots • condsurv
9 |
10 |
11 |
12 |
13 |
14 |
15 |
16 |
20 |
21 |
22 |
23 |
24 |
25 |
80 |
81 |
82 |
83 |
84 |
85 |
86 |
87 |
89 | The condsurv package contains a function for generating conditional survival estimates with associated confidence intervals, and a function for plotting conditional survival curves.
90 |
91 | Installation
92 |
93 | Install the package using
94 |
95 | remotes::install_github("zabore/condsurv")
96 |
97 |
98 | Documentation
99 |
100 | This package is documented using pkgdown, and the resulting website is available at http://www.emilyzabor.com/condsurv/, where detailed Tutorials can be found covering all of the package functionality.
101 | See http://www.emilyzabor.com/condsurv/reference/ for detailed function documentation.
102 |
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91 | Last updated: 2022-10-20
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94 | plot_cs.Rmd
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101 | library(condsurv)
102 | library(survival)
103 | #> Warning: package 'survival' was built under R version 4.2.1
104 | library(ggplot2)
105 | library(dplyr)
106 | #> Warning: package 'dplyr' was built under R version 4.2.1
107 | To plot the conditional survival curves at baseline, and for those
108 | who have survived 6 months, 1 year, 1.5 years, and 2 years, we use the
109 | gg_conditional_surv function.
110 | The lung dataset from the survival package
111 | will be used to illustrate.
112 |
113 | # Scale the time variable to be in years rather than days
114 | lung2 <-
115 | mutate(
116 | lung,
117 | os_yrs = time / 365.25
118 | )
119 |
120 | myfit <- survfit(Surv(os_yrs, status) ~ 1, data = lung2)
121 |
122 | cond_times <- seq(0, 2, 0.5)
123 |
124 | gg_conditional_surv(
125 | basekm = myfit,
126 | at = cond_times,
127 | main = "Conditional survival in lung data"
128 | )
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8 | Estimate conditional survival • condsurv
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87 | Estimate conditional survival
88 | Emily C.
89 | Zabor
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91 | Last updated: 2022-10-20
92 |
93 |
94 | estimate_cs.Rmd
95 |
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100 |
101 | library(condsurv)
102 | library(dplyr)
103 | #> Warning: package 'dplyr' was built under R version 4.2.1
104 | library(survival)
105 | #> Warning: package 'survival' was built under R version 4.2.1
106 | If \(S(t)\) represents the survival
107 | function at time \(t\), then
108 | conditional survival is defined as
109 | \[S(y|x) = \frac{S(x +
110 | y)}{S(x)}\]
111 | where \(y\) is the number of
112 | additional survival years of interest and \(x\) is the number of years a subject has
113 | already survived.
114 |
115 | Generating conditional survival estimates
116 |
117 | The conditional_surv_est function will generate this
118 | estimate along with 95% confidence intervals.
119 | The lung dataset from the survival package
120 | will be used to illustrate.
121 |
122 | # Scale the time variable to be in years rather than days
123 | lung2 <-
124 | mutate(
125 | lung,
126 | os_yrs = time / 365.25
127 | )
128 | First generate a single conditional survival estimate. This is the
129 | conditional survival of surviving to 1 year conditioned on already
130 | having survived 6 months (\(0.5\)
131 | year). This returns a list, where cs_est is the conditional
132 | survival estimate, cs_lci is the lower bound of the 95%
133 | confidence interval and cs_uci is the upper bound of the
134 | 95% confidence interval.
135 |
136 | myfit <- survfit(Surv(os_yrs, status) ~ 1, data = lung2)
137 |
138 | conditional_surv_est(
139 | basekm = myfit,
140 | t1 = 0.5,
141 | t2 = 1
142 | )
143 | #> $cs_est
144 | #> [1] 0.58
145 | #>
146 | #> $cs_lci
147 | #> [1] 0.49
148 | #>
149 | #> $cs_uci
150 | #> [1] 0.66
151 | You can easily use purrr::map_df to get a table of
152 | estimates for multiple timepoints. For example we could get the
153 | conditional survival estimate of surviving to a variety of different
154 | time points given that the subject has already survived for 6 months
155 | (0.5 years).
156 |
157 | prob_times <- seq(1, 2.5, 0.5)
158 |
159 | purrr::map_df(
160 | prob_times,
161 | ~conditional_surv_est(
162 | basekm = myfit,
163 | t1 = 0.5,
164 | t2 = .x)
165 | ) %>%
166 | dplyr::mutate(years = prob_times) %>%
167 | dplyr::select(years, everything()) %>%
168 | knitr::kable()
169 |
170 |
171 | years
172 | cs_est
173 | cs_lci
174 | cs_uci
175 |
176 |
177 |
178 | 1.0
179 | 0.58
180 | 0.49
181 | 0.66
182 |
183 |
184 | 1.5
185 | 0.36
186 | 0.27
187 | 0.45
188 |
189 |
190 | 2.0
191 | 0.16
192 | 0.10
193 | 0.25
194 |
195 |
196 | 2.5
197 | 0.07
198 | 0.02
199 | 0.15
200 |
201 |
202 |
203 |
204 |
205 | A note on confidence interval estimation
206 |
207 | The confidence intervals are based on a variation of the log-log
208 | transformation, also known as the “exponential” Greenwood formula, where
209 | the conditional survival estimate is substituted in for the traditional
210 | survival estimate in constructing the confidence interval.
211 | If \(\hat{S}(y|x)\) is the estimated
212 | conditional survival to \(y\) given
213 | having already survived to \(x\),
214 | then
215 | \[\hat{S}(y|x)^{exp(\pm1.96\sqrt{\hat{L}(y|x)})}\]
216 | where
217 | \[\hat{L}(y|x)=\frac{1}{\log(\hat{S}(y|x))^2}\sum_{j:x
218 | \leq \tau_j \leq y}\frac{d_j}{(r_j-d_j)r_j}\]
219 | and
220 | \(\tau_j\) = distinct death time
221 | \(j\)
222 | \(d_j\) = number of failures at
223 | death time \(j\)
224 | \(r_j\) = number at risk at death
225 | time \(j\)
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1 | #' Interactive conditional survival Shiny app
2 | #'
3 | #' \code{condKMapp} produces an interactive Shiny app containing output from
4 | #' both the \code{condKMest} function and the \code{condKMplot} function.
5 | #'
6 | #' @param .basekm \code{survfit} object
7 | #'
8 | #' @return The output includes 1) a plot with one overall Kaplan-Meier survival
9 | #' curve and an additional Kaplan-Meier survival curve for each time on which
10 | #' you are conditioning and 2) a table with conditional survival estimates at
11 | #' the specified survival time along with their associated confidence intervals,
12 | #' with one row for each time on which you are conditioning.
13 | #'
14 | #' @export
15 | #'
16 |
17 | condKMapp <- function(.basekm) {
18 | library(shiny)
19 | library(survival)
20 |
21 | shinyApp(
22 | ui = fluidPage(
23 | pageWithSidebar(
24 | headerPanel("Conditional survival estimates"),
25 | sidebarPanel(
26 | helpText("This interactive interface allows you to adjust the
27 | survival time of interest (using the numeric entry box below)
28 | and to select a single survival time, or range of survival times, on
29 | which to condition (using the slider bar below)."),
30 | numericInput(
31 | inputId = "survtime", label = "Enter the survival time of interest (in years)",
32 | value = 5, min = 0, max = 10
33 | ),
34 | sliderInput(
35 | inputId = "condtime", label = "Select the range of times on which to condition (in years)",
36 | value = c(1, 4), min = 1, max = 10, step = 1
37 | ),
38 | helpText("The output includes 1) a plot with one overall Kaplan-Meier
39 | survival curve and an additional Kaplan-Meier survival curve for
40 | each time on which you are conditioning and 2) a table
41 | with conditional survival estimates at the specified
42 | survival time along with their associated confidence intervals,
43 | with one row for each time on which you are conditioning.")
44 | ),
45 | mainPanel(
46 | plotOutput(outputId = "condplot"),
47 | tableOutput(outputId = "condtab")
48 | )
49 | )
50 | ),
51 |
52 | server = function(input, output) {
53 | output$condplot <- renderPlot({
54 | if (class(.basekm) != "survfit") {
55 | stop("Argument to .basekm must be of class survfit")
56 | }
57 | if (max(input$condtime) > max(.basekm$time)) {
58 | stop(paste(
59 | "The range of times on which to condition specifies value(s) outside the range of observed times;",
60 | "the maximum observed time is", round(max(.basekm$time), 2)
61 | ))
62 | }
63 |
64 | plot(.basekm,
65 | conf.int = F, xlab = "Years", ylab = "Survival probability",
66 | lwd = 1, mark.time = F, main = "Conditional survival curves"
67 | )
68 | at <- seq(from = min(input$condtime), to = max(input$condtime), by = 1)
69 | nt <- length(at)
70 | fitkm <- list()
71 | for (i in 1:nt) {
72 | fitkm[[i]] <- survfit(
73 | formula = as.formula(.basekm$call$formula),
74 | data = eval(.basekm$call$data),
75 | start.time = at[i]
76 | )
77 | lines(fitkm[[i]], conf.int = F, col = i + 1, lwd = 1, mark.time = F)
78 | abline(v = i, lty = 3)
79 | }
80 | }, height = 400, width = 500)
81 |
82 | output$condtab <- renderTable({
83 | if (class(.basekm) != "survfit") {
84 | stop("Argument to .basekm must be of class survfit")
85 | }
86 | if (max(input$condtime) > max(.basekm$time)) {
87 | stop(paste(
88 | "The range of times on which to condition specifies value(s) outside the range of observed times;",
89 | "the maximum observed time is", round(max(.basekm$time), 2)
90 | ))
91 | }
92 | if (max(input$survtime) > max(.basekm$time)) {
93 | stop(paste(
94 | "The survival time of interest specifies a value outside the range of observed times;",
95 | "the maximum observed time is", round(max(.basekm$time), 2)
96 | ))
97 | }
98 |
99 | at <- seq(from = min(input$condtime), to = max(input$condtime), by = 1)
100 | nt <- length(at)
101 | cstab <- NULL
102 | for (i in 1:nt) {
103 | if (at[i] < input$survtime) {
104 | cs <- summary(.basekm, times = c(at[i], input$survtime))$surv[2] /
105 | summary(.basekm, times = c(at[i], input$survtime))$surv[1]
106 | cs.sq <- cs^2
107 | d <- .basekm$n.event[.basekm$time >= at[i] & .basekm$time <= input$survtime &
108 | .basekm$n.event > 0]
109 | r <- .basekm$n.risk[.basekm$time >= at[i] & .basekm$time <= input$survtime &
110 | .basekm$n.event > 0]
111 | dr <- d / (r * (r - d))
112 | var.cs <- cs.sq * sum(dr)
113 | ci <- cs + c(-1, 1) * (qnorm(0.975) * sqrt(var.cs))
114 | if (ci[1] < 0) {
115 | warning("Lower bound of CI has been truncated to 0")
116 | }
117 | if (ci[2] > 1) {
118 | warning("Upper bound of CI has been truncated to 1")
119 | }
120 | ci.cs <- round(ci, 2)
121 | if (ci.cs[1] < 0) {
122 | ci.cs[1] <- 0
123 | }
124 | if (ci.cs[2] > 1) {
125 | ci.cs[2] <- 1
126 | }
127 | cstab <- data.frame(rbind(cstab, c(round(at[i]), round(cs, 2), ci.cs[1], ci.cs[2])),
128 | stringsAsFactors = FALSE
129 | )
130 | }
131 | }
132 | colnames(cstab) <- c("Conditional time", "Conditional survival", "Lower 95% CI", "Upper 95% CI")
133 | cstab
134 | }, digits = c(0, 0, 2, 2, 2))
135 | }
136 | )
137 | }
138 |
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65 | Reference
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66 | All functions67 | 68 | |
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| 70 | 71 | | 72 |Estimate conditional survival with a 95% confidence interval |
73 |
| 74 | 75 | | 76 |Generate conditional survival plots using ggplot2 |
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68 | Estimate conditional survival with a 95% confidence interval
64 | 65 |conditional_surv_est.Rd
69 |
72 |
73 | conditional_surv_est estimates the Kaplan-Meier conditional survival at
70 | fixed time points and produces a 95% confidence interval
74 |
75 |
76 |
77 | conditional_surv_est(basekm, t1, t2)
78 |
91 | Arguments
79 |- basekm 80 |
survfitobject
81 |
82 |
83 | - t1 84 |
the time on which to condition
85 |
86 |
87 | - t2 88 |
the survival time to estimate
89 |
90 |
92 |
99 | Value
93 | 94 | 95 |A list where cs_est is the conditional survival estimate,
96 | cs_lci is the lower bound of the 95% confidence interval and
97 | cs_uci is the upper bound of the 95% confidence interval
100 |
108 |
109 | Details
101 |For example, if t1 = 2 and t2 = 5, the function
102 | will return the probability of surviving to year 5 conditioned on having
103 | already survived to year 2. See the vignette
104 | at http://www.emilyzabor.com/condsurv/articles/estimate_cs.html for
105 | details on calculations of conditional survival estimates and confidence
106 | intervals, and examples.
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68 | Generate conditional survival plots using ggplot2
64 | 65 |gg_conditional_surv.Rd
69 |
72 |
73 | gg_conditional_surv produces a Kaplan-Meier plot for a variety of times on
70 | which to condition using ggplot2
74 |
82 |
83 |
84 | gg_conditional_surv(
75 | basekm,
76 | at,
77 | main = NULL,
78 | xlab = "Years",
79 | ylab = "Survival probability",
80 | lwd = 1
81 | )
85 |
110 | Arguments
86 |- basekm 87 |
survfitobject
88 |
89 |
90 | - at 91 |
vector of times on which to condition
92 |
93 |
94 | - main 95 |
plot title
96 |
97 |
98 | - xlab 99 |
x-axis label
100 |
101 |
102 | - ylab 103 |
y-axis label, defaults to "Survival probability"
104 |
105 |
106 | - lwd 107 |
plot line width, defaults to 1
108 |
109 |
111 |
123 |
124 | Value
112 | 113 | 114 |A ggplot with a line for the overall Kaplan-Meier plot and one
115 | additional line for each value in at
#' @details See the vignette 120 | at http://www.emilyzabor.com/condsurv/articles/plot_cs.html for 121 | details and examples.
122 |
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340 | padding: 1px;
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343 | /* vertical spacing after htmlwidgets */
344 | .html-widget {
345 | margin-bottom: 10px;
346 | }
347 |
348 | /* fontawesome ------------------------ */
349 |
350 | .fab {
351 | font-family: "Font Awesome 5 Brands" !important;
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353 |
354 | /* don't display links in code chunks when printing */
355 | /* source: https://stackoverflow.com/a/10781533 */
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364 | */
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366 | div.csl-bib-body { }
367 | div.csl-entry {
368 | clear: both;
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370 | .hanging-indent div.csl-entry {
371 | margin-left:2em;
372 | text-indent:-2em;
373 | }
374 | div.csl-left-margin {
375 | min-width:2em;
376 | float:left;
377 | }
378 | div.csl-right-inline {
379 | margin-left:2em;
380 | padding-left:1em;
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382 | div.csl-indent {
383 | margin-left: 2em;
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385 |
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104 | The condsurv package contains a function for generating conditional survival estimates with associated confidence intervals, and a function for plotting conditional survival curves.
91 |
96 |
97 | Installation 92 |
93 |Install the package using
94 |
95 | remotes::install_github("zabore/condsurv")
98 |
103 | Documentation 99 |
100 |This package is documented using pkgdown, and the resulting website is available at http://www.emilyzabor.com/condsurv/, where detailed Tutorials can be found covering all of the package functionality.
101 |See http://www.emilyzabor.com/condsurv/reference/ for detailed function documentation.
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2 | /*
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7 | .algolia-autocomplete {
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10 | -ms-flex: 1;
11 | flex: 1
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13 |
14 | .algolia-autocomplete .ds-dropdown-menu {
15 | width: 100%;
16 | min-width: none;
17 | max-width: none;
18 | padding: .75rem 0;
19 | background-color: #fff;
20 | background-clip: padding-box;
21 | border: 1px solid rgba(0, 0, 0, .1);
22 | box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175);
23 | }
24 |
25 | @media (min-width:768px) {
26 | .algolia-autocomplete .ds-dropdown-menu {
27 | width: 175%
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29 | }
30 |
31 | .algolia-autocomplete .ds-dropdown-menu::before {
32 | display: none
33 | }
34 |
35 | .algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] {
36 | padding: 0;
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82 | .algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header {
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109 | font-weight: 400;
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111 | }
112 |
113 | .algolia-autocomplete .algolia-docsearch-footer {
114 | width: 110px;
115 | height: 20px;
116 | z-index: 3;
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122 |
123 | .algolia-autocomplete .algolia-docsearch-footer--logo {
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100 | Plot conditional survival curves
88 |Emily C. 89 | Zabor
90 | 91 |Last updated: 2022-10-20
92 | 93 | 94 |plot_cs.Rmd
101 | library(condsurv)
102 | library(survival)
103 | #> Warning: package 'survival' was built under R version 4.2.1
104 | library(ggplot2)
105 | library(dplyr)
106 | #> Warning: package 'dplyr' was built under R version 4.2.1To plot the conditional survival curves at baseline, and for those
108 | who have survived 6 months, 1 year, 1.5 years, and 2 years, we use the
109 | gg_conditional_surv function.
The lung dataset from the survival package
111 | will be used to illustrate.
113 | # Scale the time variable to be in years rather than days
114 | lung2 <-
115 | mutate(
116 | lung,
117 | os_yrs = time / 365.25
118 | )
120 | myfit <- survfit(Surv(os_yrs, status) ~ 1, data = lung2)
121 |
122 | cond_times <- seq(0, 2, 0.5)
123 |
124 | gg_conditional_surv(
125 | basekm = myfit,
126 | at = cond_times,
127 | main = "Conditional survival in lung data"
128 | )
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100 | Estimate conditional survival
88 |Emily C. 89 | Zabor
90 | 91 |Last updated: 2022-10-20
92 | 93 | 94 |estimate_cs.Rmd
101 | library(condsurv)
102 | library(dplyr)
103 | #> Warning: package 'dplyr' was built under R version 4.2.1
104 | library(survival)
105 | #> Warning: package 'survival' was built under R version 4.2.1If \(S(t)\) represents the survival 107 | function at time \(t\), then 108 | conditional survival is defined as
109 |\[S(y|x) = \frac{S(x + 110 | y)}{S(x)}\]
111 |where \(y\) is the number of 112 | additional survival years of interest and \(x\) is the number of years a subject has 113 | already survived.
114 |
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203 |
204 | Generating conditional survival estimates 116 |
117 |The conditional_surv_est function will generate this
118 | estimate along with 95% confidence intervals.
The lung dataset from the survival package
120 | will be used to illustrate.
122 | # Scale the time variable to be in years rather than days
123 | lung2 <-
124 | mutate(
125 | lung,
126 | os_yrs = time / 365.25
127 | )First generate a single conditional survival estimate. This is the
129 | conditional survival of surviving to 1 year conditioned on already
130 | having survived 6 months (\(0.5\)
131 | year). This returns a list, where cs_est is the conditional
132 | survival estimate, cs_lci is the lower bound of the 95%
133 | confidence interval and cs_uci is the upper bound of the
134 | 95% confidence interval.
136 | myfit <- survfit(Surv(os_yrs, status) ~ 1, data = lung2)
137 |
138 | conditional_surv_est(
139 | basekm = myfit,
140 | t1 = 0.5,
141 | t2 = 1
142 | )
143 | #> $cs_est
144 | #> [1] 0.58
145 | #>
146 | #> $cs_lci
147 | #> [1] 0.49
148 | #>
149 | #> $cs_uci
150 | #> [1] 0.66You can easily use purrr::map_df to get a table of
152 | estimates for multiple timepoints. For example we could get the
153 | conditional survival estimate of surviving to a variety of different
154 | time points given that the subject has already survived for 6 months
155 | (0.5 years).
157 | prob_times <- seq(1, 2.5, 0.5)
158 |
159 | purrr::map_df(
160 | prob_times,
161 | ~conditional_surv_est(
162 | basekm = myfit,
163 | t1 = 0.5,
164 | t2 = .x)
165 | ) %>%
166 | dplyr::mutate(years = prob_times) %>%
167 | dplyr::select(years, everything()) %>%
168 | knitr::kable()| years | 172 |cs_est | 173 |cs_lci | 174 |cs_uci | 175 |
|---|---|---|---|
| 1.0 | 179 |0.58 | 180 |0.49 | 181 |0.66 | 182 |
| 1.5 | 185 |0.36 | 186 |0.27 | 187 |0.45 | 188 |
| 2.0 | 191 |0.16 | 192 |0.10 | 193 |0.25 | 194 |
| 2.5 | 197 |0.07 | 198 |0.02 | 199 |0.15 | 200 |
205 |
227 | A note on confidence interval estimation 206 |
207 |The confidence intervals are based on a variation of the log-log 208 | transformation, also known as the “exponential” Greenwood formula, where 209 | the conditional survival estimate is substituted in for the traditional 210 | survival estimate in constructing the confidence interval.
211 |If \(\hat{S}(y|x)\) is the estimated 212 | conditional survival to \(y\) given 213 | having already survived to \(x\), 214 | then
215 |\[\hat{S}(y|x)^{exp(\pm1.96\sqrt{\hat{L}(y|x)})}\]
216 |where
217 |\[\hat{L}(y|x)=\frac{1}{\log(\hat{S}(y|x))^2}\sum_{j:x 218 | \leq \tau_j \leq y}\frac{d_j}{(r_j-d_j)r_j}\]
219 |and
220 |\(\tau_j\) = distinct death time 221 | \(j\)
222 |\(d_j\) = number of failures at 223 | death time \(j\)
224 |\(r_j\) = number at risk at death 225 | time \(j\)
226 |