Kaplan-Meier Plot with 'ggplot2': 'survfit' and 'svykm' objects from 'survival' and 'survey' packages.
knitr::opts_chunk$set(echo = TRUE, message = F, warning = F, fig.path = "man/figures/README-") library(jskm)
install.packages("jskm") ## From github: latest version install.packages("remotes") remotes::install_github("jinseob2kim/jskm") library(jskm)
# Load dataset library(survival) data(colon) fit <- survfit(Surv(time, status) ~ rx, data = colon) # Plot the data jskm(fit) jskm(fit, table = T, pval = T, label.nrisk = "No. at risk", size.label.nrisk = 8, xlabs = "Time(Day)", ylabs = "Survival", ystratalabs = c("Obs", "Lev", "Lev + 5FU"), ystrataname = "rx", marks = F, timeby = 365, xlims = c(0, 3000), ylims = c(0.25, 1), showpercent = T )
jskm(fit, ci = T, cumhaz = T, mark = F, ylab = "Cumulative incidence (%)", surv.scale = "percent", pval = T, pval.size = 6, pval.coord = c(300, 0.7))
jskm(fit, mark = F, surv.scale = "percent", pval = T, table = T, cut.landmark = 500) jskm(fit, mark = F, surv.scale = "percent", pval = T, table = T, cut.landmark = 500, showpercent = T)
status2
variable: 0 - censoring, 1 - event, 2 - competing risk
## Make competing risk variable, Not real colon$status2 <- colon$status colon$status2[1:400] <- 2 colon$status2 <- factor(colon$status2) fit2 <- survfit(Surv(time, status2) ~ rx, data = colon) jskm(fit2, mark = F, surv.scale = "percent", table = T, status.cmprsk = "1") jskm(fit2, mark = F, surv.scale = "percent", table = T, status.cmprsk = "1", showpercent = T, cut.landmark = 500)
jskm(fit, theme = "jama", cumhaz = T, table = T, mark = F, ylab = "Cumulative incidence (%)", surv.scale = "percent", pval = T, pval.size = 6, pval.coord = c(300, 0.7))
jskm(fit, theme = "nejm", nejm.infigure.ratiow = 0.7, nejm.infigure.ratioh = 0.4, nejm.infigure.ylim = c(0, 0.7), cumhaz = T, table = T, mark = F, ylab = "Cumulative incidence (%)", surv.scale = "percent", pval = T, pval.size = 6, pval.coord = c(300, 0.7))
svykm.object
in survey packagelibrary(survey) data(pbc, package = "survival") pbc$randomized <- with(pbc, !is.na(trt) & trt > 0) biasmodel <- glm(randomized ~ age * edema, data = pbc) pbc$randprob <- fitted(biasmodel) dpbc <- svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, randomized)) s1 <- svykm(Surv(time, status > 0) ~ 1, design = dpbc) s2 <- svykm(Surv(time, status > 0) ~ sex, design = dpbc) svyjskm(s1) svyjskm(s2, pval = T, table = T, design = dpbc) svyjskm(s2, cumhaz = T, ylab = "Cumulative incidence (%)", surv.scale = "percent", pval = T, design = dpbc, pval.coord = c(300, 0.7), showpercent = T)
If you want to get confidence interval, you should apply se = T
option to svykm
object.
s3 <- svykm(Surv(time, status > 0) ~ sex, design = dpbc, se = T) svyjskm(s3) svyjskm(s3, ci = F) svyjskm(s3, ci = F, surv.scale = "percent", pval = T, table = T, cut.landmark = 1000, showpercent = T)
svyjskm(s2, theme = "jama", pval = T, table = T, design = dpbc)
svyjskm(s2, theme = "nejm", nejm.infigure.ratiow = 0.45, nejm.infigure.ratioh = 0.4, nejm.infigure.ylim = c(0.2, 1), pval = T, table = T, design = dpbc)
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