survdiff {survival4}R Documentation

Test Survival Curve Differences

Usage

survdiff(formula, data,  rho=0, subset)

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. 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.
rho a parameter that controls the type of test.
subset subset of the observations to be used in the fit.
n the number of subjects in each group.
obs the weighted observed number of events in each group.
exp the weighted expected number of events in each group.
chisq the chisquare statistic for a test of equality.

Value

a list with components:

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. When rho = 0 this is the log-rank or Mantel-Haenszel test, and when 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.

References

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

See Also

survdiff.print.

Examples

data(ovarian)
survdiff(Surv(futime, fustat) ~ rx)
dontrun{
library(ratetables)
expect <- survexp(entry, birth, sex, futime)
survdiff(Surv(futime, fustat) ~ offset(expect$surv))  #One sample log-rank
}


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