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

"summary"(object, times, newdata, max.tables = 20, surv = TRUE, cause, intervals = FALSE, percent = FALSE, showTime = TRUE, asMatrix = FALSE, ...)

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

The cause for predicting the cause-specific cumulative incidence function in competing risk models.

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.

Value

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

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 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,collapse=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


# '