survdiffTerms {psichomics} | 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

    survdiffTerms(survTerms, ...)

### Arguments

`survTerms` | `survTerms` object: survival terms obtained after running `processSurvTerms` (see examples)
`...` | Arguments passed on to `survival::survdiff` 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

    clinical <- read.table(text = "2549   NA ii  female
                                    840   NA i   female
                                     NA 1204 iv    male
                                     NA  383 iv  female
                                   1293   NA iii   male
                                     NA 1355 ii    male")
    names(clinical) <- c("patient.days_to_last_followup",
                         "patient.days_to_death",
                         "patient.stage_event.pathologic_stage",
                         "patient.gender")
    timeStart  <- "days_to_death"
    event      <- "days_to_death"
    formulaStr <- "patient.stage_event.pathologic_stage + patient.gender"
    survTerms  <- processSurvTerms(clinical, censoring="right", event, timeStart,
                                   formulaStr=formulaStr)
    survdiffTerms(survTerms)

[Package psichomics version 1.10.2 Index]