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jskm

Kaplan-Meier Plot with ‘ggplot2’: ‘survfit’ and ‘svykm’ objects from ‘survival’ and ‘survey’ packages.

Install

install.packages("jskm")

## From github: latest version
install.packages("remotes")
remotes::install_github("jinseob2kim/jskm")

library(jskm)

Example

Survival probability

# 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
)

Cumulative hazard: 1- Survival probability

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))

Landmark analysis

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)

Competing risk analysis

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)

Theme

JAMA

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))

NEJM

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))

Weighted Kaplan-Meier plot - svykm.object in survey package

library(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)

Theme

JAMA

svyjskm(s2, theme='jama', pval = T,  table = T, design = dpbc)

NEJM

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)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.