| survdiff {survival} | R Documentation |
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
|
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 |
rho |
a scalar parameter that controls the type of test. |
timefix |
process times through the |
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. |
pvalue |
the p-value corresponding to the Chisquare statistic |
Description
This function implements the G-rho family of
Harrington and Fleming (1982), with weights on each death of S(t)^\rho,
where S(t) 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.
Peto and Peto show that the Gehan-Wilcoxon test can be badly biased if the two groups have different censoring patterns, and proposed an alternative. Prentice and Marek later showed an actual example where this issue occurs. For most data sets the Gehan-Wilcoxon and Peto-Peto-Prentice variant will hardly differ, however.
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 to recode the righ-hand-side covariate.
References
Harrington, D. P. and Fleming, T. R. (1982). A class of rank test procedures for censored survival data. Biometrika, 553-566.
Peto R. Peto and Peto, J. (1972) Asymptotically efficient rank invariant test procedures (with discussion), JRSSA, 185-206.
Prentice, R. and Marek, P. (1979) A qualitative discrepancy between censored data rank tests, Biometics, 861–867.
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))
# The free light chain data set is close to the population.
e2 <- survexp(futime ~ 1, data=flchain, cohort=FALSE,
rmap= list(age= age*365.25, sex=sex,
year=as.Date(paste0(sample.yr, "-07-01"))),
ratetable= survexp.mn)
survdiff(Surv(futime, death) ~ offset(e2), flchain)