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.

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

survfit object. See survfit.object for details. Methods defined for survfit objects are print, plot, lines, and points.

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

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)
#> Call:
#> survdiff(formula = survTerms$form, data = survTerms$survTime)
#> 
#>                                                                 N Observed
#> patient.stage_event.pathologic_stage=i, patient.gender=female   1        0
#> patient.stage_event.pathologic_stage=ii, patient.gender=female  1        0
#> patient.stage_event.pathologic_stage=ii, patient.gender=male    1        1
#> patient.stage_event.pathologic_stage=iii, patient.gender=male   1        0
#> patient.stage_event.pathologic_stage=iv, patient.gender=female  1        1
#> patient.stage_event.pathologic_stage=iv, patient.gender=male    1        1
#>                                                                 Expected
#> patient.stage_event.pathologic_stage=i, patient.gender=female      0.167
#> patient.stage_event.pathologic_stage=ii, patient.gender=female     0.917
#> patient.stage_event.pathologic_stage=ii, patient.gender=male       0.917
#> patient.stage_event.pathologic_stage=iii, patient.gender=male      0.417
#> patient.stage_event.pathologic_stage=iv, patient.gender=female     0.167
#> patient.stage_event.pathologic_stage=iv, patient.gender=male       0.417
#>                                                                 (O-E)^2/E
#> patient.stage_event.pathologic_stage=i, patient.gender=female     0.16667
#> patient.stage_event.pathologic_stage=ii, patient.gender=female    0.91667
#> patient.stage_event.pathologic_stage=ii, patient.gender=male      0.00758
#> patient.stage_event.pathologic_stage=iii, patient.gender=male     0.41667
#> patient.stage_event.pathologic_stage=iv, patient.gender=female    4.16667
#> patient.stage_event.pathologic_stage=iv, patient.gender=male      0.81667
#>                                                                 (O-E)^2/V
#> patient.stage_event.pathologic_stage=i, patient.gender=female       0.200
#> patient.stage_event.pathologic_stage=ii, patient.gender=female      1.458
#> patient.stage_event.pathologic_stage=ii, patient.gender=male        0.012
#> patient.stage_event.pathologic_stage=iii, patient.gender=male       0.532
#> patient.stage_event.pathologic_stage=iv, patient.gender=female      5.000
#> patient.stage_event.pathologic_stage=iv, patient.gender=male        1.043
#> 
#>  Chisq= 7.3  on 5 degrees of freedom, p= 0.2