| survdiff {survival} | R Documentation |
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.
survdiff(formula, data, subset, na.action, rho=0)
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()$na.action.
|
rho |
a scalar parameter that controls the type of test. |
a list with components:
n |
the number of subjects in each group. |
obs |
the weighted observed number of events in each group. If there are strata, this will be a matrix with one column per stratum. |
exp |
the weighted expected number of events in each group. If there are strata, this will be a matrix with one column per stratum. |
chisq |
the chisquare statistic for a test of equality. |
var |
the variance matrix of the test. |
strata |
optionally, the number of subjects contained in each stratum. |
This function implements the G-rho family of
Harrington and Fleming (1982), with weights on each death of S(t)^rho,
where S is the Kaplan-Meier estimate of survival.
With rho = 0 this is the log-rank or Mantel-Haenszel test,
and with rho = 1 it is equivalent to the Peto & Peto modification
of the Gehan-Wilcoxon test.
If the right hand side of the formula consists only of an offset term,
then a one sample test is done.
To cause missing values in the predictors to be treated as a separate
group, rather than being omitted, use the factor function with its
exclude argument.
Harrington, D. P. and Fleming, T. R. (1982). A class of rank test procedures for censored survival data. Biometrika 69, 553-566.
## Two-sample test data(ovarian) survdiff(Surv(futime, fustat) ~ rx,data=ovarian) rm(ovarian) ## Stratified 7-sample test data(lung) survdiff(Surv(time, status) ~ pat.karno + strata(inst), data=lung) rm(lung) data(heart) data(ratetables) ## Expected survival for heart transplant patients based on ## US mortality tables expect <- survexp(futime ~ ratetable(age=(accept.dt - birth.dt), sex=1,year=accept.dt,race="white"), jasa, cohort=FALSE, ratetable=survexp.usr) ## actual survival is much worse (no surprise) print(survdiff(Surv(jasa$futime, jasa$fustat) ~ offset(expect))) rm(jasa,jasa1,heart,survexp.az,survexp.azr,survexp.fl,survexp.flr,survexp.mn,survexp.mnwhite,survexp.us,survexp.usr,survexp.wnc)