survival-analysis

问题 I am trying to create a Kaplan-Meier plot with 95% confidence bands plus having the censored data in a table beneath it. I can create the plot, but not the table. I get the error message: Error in grid.draw(both) : object 'both' not found. library(survival) library(ggplot2) library(GGally) library(gtable) data(lung) sf.sex <- survfit(Surv(time, status) ~ sex, data = lung) pl.sex <- ggsurv(sf.sex) + geom_ribbon(aes(ymin=low,ymax=up,fill=group),alpha=0.3) + guides(fill=guide_legend("sex")) pl

问题 I am trying to create a Kaplan-Meier plot with 95% confidence bands plus having the censored data in a table beneath it. I can create the plot, but not the table. I get the error message: Error in grid.draw(both) : object 'both' not found. library(survival) library(ggplot2) library(GGally) library(gtable) data(lung) sf.sex <- survfit(Surv(time, status) ~ sex, data = lung) pl.sex <- ggsurv(sf.sex) + geom_ribbon(aes(ymin=low,ymax=up,fill=group),alpha=0.3) + guides(fill=guide_legend("sex")) pl

问题 I've obtained Schoenfeld residuals for testing PH for Fine & Gray's Cox regression model in presence of competing events with cmprsk. Here's the code: fg<-crr( fu_m, event, diabetes, failcode=1, cencode=0, na.action=na.omit, gtol=1e-06, maxiter=10, variance=TRUE) fg$res fg$uft par(mfrow = c(1,1), mar = c(4.5,4,2,1)) for(j in 1:ncol(fg$res)) fg$u scatter.smooth(fg$uftime, fg$res[,j], main = names(fg$diabetes)[j], xlab = "Failure time", ylab = "Schoenfeld residuals") Here's the output: > fg$res

问题 I am using mlr and would like to be able to use the extended version of the Cox PH model for right-censored, time dependent covariates. This is what I have tried, following the vignette on time-dependent covariates https://cran.microsoft.com/web/packages/survival/vignettes/timedep.pdf (section 3.4): library(survival) library(mlr) data(pbc) temp <- subset(pbc, id <= 312, select=c(id:sex, stage)) # baseline pbc2 <- tmerge(temp, temp, id=id, death = event(time, status)) #set range pbc2 <- tmerge

问题 there is quite a lot of information (internet and textbooks) on how to do survival analysis in R with the survival package. But I don't find any information on how to do this when you have left censored data. Problem background: I have a self constructed data set with published survival data. Usually the event time and the date of the last follow-up (right censoring) is given. There is however one study that only states that the event happened before day 360. So I left censored this data.

问题 I'm trying to plot survival curves for several variables facet by the variable sex with the ggsurvplot_facet() function. When I apply my code to a single fitted model, it works fine. However, when I try to use the same code within a function or within a for loop, it fails to plot all the survival curves that should be plotted and returns an error. I would perform this plotting in ggsurvplot_facet() itself if it allowed as input a list of survfit elements, in the same way ggsurvplot() does,

问题 How can I plot survival curves for representative values of a continuous covariate in a cox proportional hazards model? Specifically, I would like to do this in ggplot using a "survfit.cox" "survfit" object. This may seem like a question that has already been answered, but I have searched through everything in SO with the terms 'survfit' and 'newdata' (plus many other search terms). This is the thread that comes closest to answering my question so far: Plot Kaplan-Meier for Cox regression In

问题 I am currently working on a meta-analysis of survival data across several clinical trials. To do this, I have code from a published analysis using the same methodology. However, when running this code using the data from the published analysis, I am unable to replicate their results. In fact, the results fail to converge to any kind of reasonable estimate. The code itself (not including the data) should be correct as it comes directly from the authors. I assume the problem has to do w/

问题 I'm having some trouble using coxph(). I've two categorical variables:"tecnologia" and "pais", and I want to evaluate the possible interaction effect of "pais" on "tecnologia"."tecnologia" is a variable factor with 2 levels: gps and convencional. And "pais" as 2 levels: PT and ES. I have no idea why this warning keeps appearing. Here's the code and the output: cox_AC<-coxph(Surv(dados_temp$dias_seg,dados_temp$status)~tecnologia*pais,data=dados_temp) Warning message: In coxph(Surv(dados_temp

问题 A little background that might be useful. I am in a position that I have two plots that need to be combined. I have used the "survival" package to generate a Kaplan-Meier curve from a set of data. I have also created and used a custom function to generate survival curves (from the same data, after being processed by WinBUGS) I generate my K-M curve in the following way. library("survminer") library("survival") KM.est <- survfit(Surv(IPD.data[,1],IPD.data[,2])~1, data=IPD.data,type="kaplan