summary.prodlim: Summary method for prodlim objects.

Description

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.

Usage

# S3 method for prodlim
summary(object, times, newdata, max.tables = 20,
  surv = TRUE, cause, intervals = FALSE, percent = FALSE,
  showTime = TRUE, asMatrix = FALSE, ...)

Value

A data.frame with the relevant information.

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.
showTime: If TRUE evaluation times are put into a column of the output table, otherwise evaluation times are shown as rownames.
asMatrix: Control the output format when there are multiple life tables, either because of covariate strata or competing causes or both. If not missing and not FALSE, reduce multiple life tables into a matrix with new columns X for covariate strata and Event for competing risks.
...: Further arguments that are passed to the print function.

Author

Thomas A. Gerds tag@biostat.ku.dk

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

Run this code


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)

summary(fit1,times=c(1,5,10),asMatrix=TRUE,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,asMatrix=TRUE)
summary(crfit,times=c(1,2,5),cause=1:2,asMatrix=TRUE)


# 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


# '

Run the code above in your browser using DataCamp Workspace