survival: survdiff – R documentation

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survdiff

Test Survival Curve Differences

Description

Tests if there is a difference between two or more survival curves using the G-rho family of tests, or for a single curve against a known alternative.

Usage

survdiff(formula, data, subset, na.action, rho=0, timefix=TRUE)

Arguments

`formula`	a formula expression as for other survival models, of the form `Surv(time, status) ~ predictors`. For a one-sample test, the predictors must consist of a single `offset(sp)` term, where `sp` is a vector giving the survival probability of each subject. For a k-sample test, each unique combination of predictors defines a subgroup. A `strata` term may be used to produce a stratified test. To cause missing values in the predictors to be treated as a separate group, rather than being omitted, use the `strata` function with its `na.group=T` argument.
`data`	an optional data frame in which to interpret the variables occurring in the formula.
`subset`	expression indicating which subset of the rows of data should be used in the fit. This can be a logical vector (which is replicated to have length equal to the number of observations), a numeric vector indicating which observation numbers are to be included (or excluded if negative), or a character vector of row names to be included. All observations are included by default.
`na.action`	a missing-data filter function. This is applied to the `model.frame` after any subset argument has been used. Default is `options()$na.action`.
`rho`	a scalar parameter that controls the type of test.
`timefix`	process times through the `aeqSurv` function to eliminate potential roundoff issues.

Value

a list with components:

`n`	the number of subjects in each group.
`obs`	the weighted observed number of events in each group. If there are strata, this will be a matrix with one column per stratum.
`exp`	the weighted expected number of events in each group. If there are strata, this will be a matrix with one column per stratum.
`chisq`	the chisquare statistic for a test of equality.
`var`	the variance matrix of the test.
`strata`	optionally, the number of subjects contained in each stratum.

METHOD

This function implements the G-rho family of Harrington and Fleming (1982), with weights on each death of S(t)^rho, where S is the Kaplan-Meier estimate of survival. With rho = 0 this is the log-rank or Mantel-Haenszel test, and with rho = 1 it is equivalent to the Peto & Peto modification of the Gehan-Wilcoxon test.

If the right hand side of the formula consists only of an offset term, then a one sample test is done. To cause missing values in the predictors to be treated as a separate group, rather than being omitted, use the factor function with its exclude argument.

References

Harrington, D. P. and Fleming, T. R. (1982). A class of rank test procedures for censored survival data. Biometrika 69, 553-566.

Examples

## Two-sample test
survdiff(Surv(futime, fustat) ~ rx,data=ovarian)

## Stratified 7-sample test

survdiff(Surv(time, status) ~ pat.karno + strata(inst), data=lung)

## Expected survival for heart transplant patients based on
## US mortality tables
expect <- survexp(futime ~ 1, data=jasa, cohort=FALSE,
                  rmap= list(age=(accept.dt - birth.dt), sex=1, year=accept.dt),
                  ratetable=survexp.us)
## actual survival is much worse (no surprise)
survdiff(Surv(jasa$futime, jasa$fustat) ~ offset(expect))

survival

Survival Analysis

v3.2-11

LGPL (>= 2)

Authors

Terry M Therneau [aut, cre], Thomas Lumley [ctb, trl] (original S->R port and R maintainer until 2009), Atkinson Elizabeth [ctb], Crowson Cynthia [ctb]

Initial release

2021-04-25