survival

问题 I am trying to use mstate package in R , for which I have to use coxph function using strata command. Here is a sample code: library(mstate) tmat <- trans.illdeath() tg <- data.frame(stt=rep(0,6),sts=rep(0,6), illt=c(1,1,6,6,8,9),ills=c(1,0,1,1,0,1), dt=c(5,1,9,7,8,12),ds=c(1,1,1,1,1,1)) tg$patid <- factor(2:7,levels=1:8,labels=as.character(1:8)) tt <- matrix(c(rep(NA,6),tg$illt,tg$dt),6,3) st <- matrix(c(rep(NA,6),tg$ills,tg$ds),6,3) mslong<-msprep(time=tt,status=st,trans=tmat) models <-

问题 I have survival data in this format, with a time-varying exposure to Intervention: ID start stop status Intervention 1 2 14 0 0 2 2 5 0 0 3 2 3 0 0 3 3 10 1 1 4 5 8 0 0 5 6 10 0 0 For example, for patient ID #3: from day 2 to day 3, the patient has not yet received the intervention (Intervention = 0), but starting on day 3 and lasting until day 10 (when the patient dies), the patient has received the intervention (Intervention = 1). I thought that I could then estimate the time-varying effect

问题 I'm trying to plot an adjusted survival curve, but struggling with changing the line types by group. I'm able to customise other aspects of the plot using typical ggplot2 language, but I've hit a wall with changing line type. Example: library(survival) library(survminer) fit2 <- coxph( Surv(stop, event) ~ size + strata(rx), data = bladder ) ggadjustedcurves(fit2, variable = "rx", data = bladder, method = "average", palette = c("#E69F00", "#56B4E9"), size = 1.3, legend = "right", legend.title

问题 I'm trying to plot an adjusted survival curve, but struggling with changing the line types by group. I'm able to customise other aspects of the plot using typical ggplot2 language, but I've hit a wall with changing line type. Example: library(survival) library(survminer) fit2 <- coxph( Surv(stop, event) ~ size + strata(rx), data = bladder ) ggadjustedcurves(fit2, variable = "rx", data = bladder, method = "average", palette = c("#E69F00", "#56B4E9"), size = 1.3, legend = "right", legend.title

来源： https://stackoverflow.com/questions/63944551/two-different-results-from-coxph-in-r-using-same-stop-and-start-times-why

来源： https://stackoverflow.com/questions/63944551/two-different-results-from-coxph-in-r-using-same-stop-and-start-times-why

问题 I'm trying to make a simple shiny ap for creating kaplan-meier survival curves that are stratified by selection the user makes. When I code the KM calculation statically (with the column name thorTr) it works but the calculation and plot is static. When I replace with input$s I get ERROR:variable lengths differ (found for 'input$s') I've tried looking at other code which use as.formula and paste, but I don't understand and couldn't get to work. But I am a new R and Shiny user so maybe I didn

问题 I'm trying to make a simple shiny ap for creating kaplan-meier survival curves that are stratified by selection the user makes. When I code the KM calculation statically (with the column name thorTr) it works but the calculation and plot is static. When I replace with input$s I get ERROR:variable lengths differ (found for 'input$s') I've tried looking at other code which use as.formula and paste, but I don't understand and couldn't get to work. But I am a new R and Shiny user so maybe I didn

问题 I am trying to plot Kaplan-Meyer curve using ggsurvplot from survminer package. I'm unable to plot it when I pass a survfit object saved in a list. Let me use lung dataset as a example. Everything works below: library("survival") library("survminer") fit <- survfit(Surv(time, status) ~ sex, data = lung) ggsurvplot(fit, conf.int = TRUE, risk.table.col = "strata", palette = c("#E7B800", "#2E9FDF"), xlim = c(0, 600)) Now I do survfit on two variables and save the model result in a list. Then

问题 I was wondering if there was a way to add two arrows to the x-axis of a forest plot, similar to what is shown in this example: How to add arrows to a forest plot? (this code does not work on ggforest) Here is the sample code: library(survival) library(survminer) model <- coxph(Surv(time, status) ~ sex + rx + adhere, data = colon ) ggforest(model) colon <- within(colon, { sex <- factor(sex, labels = c("female", "male")) differ <- factor(differ, labels = c("well", "moderate", "poor")) extent <-