# survdiff

##### Test Survival Curve Differences

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.

- Keywords
- survival

##### 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:

the number of subjects in each group.

the weighted observed number of events in each group. If there are strata, this will be a matrix with one column per stratum.

the weighted expected number of events in each group. If there are strata, this will be a matrix with one column per stratum.

the chisquare statistic for a test of equality.

the variance matrix of the test.

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 \(S(t)\) 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.

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

```
# NOT RUN {
## 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))
# }
```

*Documentation reproduced from package survival, version 3.2-3, License: LGPL (>= 2)*