R: Summary method for prodlim objects.

summary.prodlim {prodlim} | R Documentation

## 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 class 'prodlim'
    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.

### 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.

### Value

A data.frame with the relevant information.

### Author(s)

Thomas A. Gerds tag@biostat.ku.dk

### See Also

`prodlim`, `summary.Hist`

### Examples

    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

    # '

[Package prodlim version 2018.04.18 Index]