# Summary method for prodlim objects. Summarizing the result of the product limit method in life-table format. Calculates the number of subjects at risk and counts events and censored observations at specified times or in specified time intervals. ```r ## S3 method for class 'prodlim' summary( object, times, newdata, max.tables = 20, surv = TRUE, cause, intervals = FALSE, percent = FALSE, format = "df", ... ) ``` ## Arguments - `object`: An object with class `prodlim' derived with `prodlim` - `times`: Vector of times at which to return the estimated probabilities. - `newdata`: A data frame with the same variable names as those that appear on the right hand side of the 'prodlim' formula. Defaults to `object$X`. - `max.tables`: Integer. If `newdata` is not given the value of `max.tables` decides about the maximal number of tables to be shown. Defaults to 20. - `surv`: Logical. If FALSE report event probabilities instead of survival probabilities. Only available for `object$model=="survival"`. - `cause`: For competing risk models. The event of interest for which predictions of the absolute risks are obtained by evaluating the cause-specific cumulative incidence functions at `times`. - `intervals`: Logical. If TRUE count events and censored in intervals between the values of `times`. - `percent`: Logical. If TRUE all estimated values are multiplied by 100 and thus interpretable on a percent scale. - `format`: Control format of output. Since May 2021, the result is a data.table and data.frame with attributes. When there are multiple covariate strata or competing risks, these are indicated by columns. Set format to `"list"` to get the old behaviour. - `...`: Further arguments that are passed to the print function. ## Returns A data.frame with the relevant information. ## Details For cluster-correlated data the number of clusters at-risk are are also given. Confidence intervals are displayed when they are part of the fitted object. ## Examples ```r library(lava) set.seed(17) m <- survModel() distribution(m,~age) <- uniform.lvm(30,80) distribution(m,~sex) <- binomial.lvm() m <- categorical(m,~z,K=3) regression(m,eventtime~age) <- 0.01 regression(m,eventtime~sex) <- -0.4 d <- sim(m,50) d$sex <- factor(d$sex,levels=c(0,1),labels=c("female","male")) d$Z <- factor(d$z,levels=c(1,0,2),labels=c("B","A","C")) # Univariate Kaplan-Meier # ----------------------------------------------------------------------------------------- fit0 <- prodlim(Hist(time,event)~1,data=d) summary(fit0) ## show survival probabilities as percentage and ## count number of events within intervals of a ## given time-grid: summary(fit0,times=c(1,5,10,12),percent=TRUE,intervals=TRUE) ## the result of summary has a print function ## which passes ... to print and print.listof sx <- summary(fit0,times=c(1,5,10,12),percent=TRUE,intervals=TRUE) print(sx,digits=3) ## show absolute risks, i.e., cumulative incidences (1-survival) summary(fit0,times=c(1,5,10,12),surv=FALSE,percent=TRUE,intervals=TRUE) # Stratified Kaplan-Meier # ----------------------------------------------------------------------------------------- fit1 <- prodlim(Hist(time,event)~sex,data=d) print(summary(fit1,times=c(1,5,10),intervals=TRUE,percent=TRUE),digits=3) # old behaviour print(summary(fit1,times=c(1,5,10),intervals=TRUE,percent=TRUE,format="list"),digits=3) summary(fit1,times=c(1,5,10),intervals=TRUE,percent=TRUE) fit2 <- prodlim(Hist(time,event)~Z,data=d) print(summary(fit2,times=c(1,5,10),intervals=TRUE,percent=TRUE),digits=3) ## Continuous strata (Beran estimator) # ----------------------------------------------------------------------------------------- fit3 <- prodlim(Hist(time,event)~age,data=d) print(summary(fit3, times=c(1,5,10), newdata=data.frame(age=c(20,50,70)), intervals=TRUE, percent=TRUE),digits=3) ## stratified Beran estimator # ----------------------------------------------------------------------------------------- fit4 <- prodlim(Hist(time,event)~age+sex,data=d) print(summary(fit4, times=c(1,5,10), newdata=data.frame(age=c(20,50,70),sex=c("female","male","male")), intervals=TRUE, percent=TRUE),digits=3) print(summary(fit4, times=c(1,5,10), newdata=data.frame(age=c(20,50,70),sex=c("female","male","male")), intervals=TRUE, percent=TRUE),digits=3) ## assess results from summary x <- summary(fit4,times=10,newdata=expand.grid(age=c(60,40,50),sex=c("male","female"))) cbind(names(x$table),do.call("rbind",lapply(x$table,round,2))) x <- summary(fit4,times=10,newdata=expand.grid(age=c(60,40,50),sex=c("male","female"))) ## Competing risks: Aalen-Johansen # ----------------------------------------------------------------------------------------- d <- SimCompRisk(30) crfit <- prodlim(Hist(time,event)~X1,data=d) summary(crfit,times=c(1,2,5)) summary(crfit,times=c(1,2,5),cause=1,intervals=TRUE) summary(crfit,times=c(1,2,5),cause=1) summary(crfit,times=c(1,2,5),cause=1:2) # extract the actual tables from the summary sumfit <- summary(crfit,times=c(1,2,5),print=FALSE) sumfit$table[[1]] # cause 1 sumfit$table[[2]] # cause 2 # ' ``` ## See Also `prodlim`, `summary.Hist` ## Author(s) Thomas A. Gerds tag@biostat.ku.dk

summary.prodlim function