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Assess Proportional Hazards Assumption Based on Supremum Test

Source: R/survival_analysis.R
assess_phregr.Rd

Obtains the standardized score processes and the simulated distribution under the null hypothesis as well as the p-values for the supremum tests.

Usage

assess_phregr(object, resample = 1000, seed = 12345)

Arguments

object

The output from the phregr call.

resample

The number of simulation samples for the supremem test.

seed

The random seed for the simulations.

Value

A list with the following components:

  • time the unique event times.

  • score_t the observed standardized score process.

  • score_t_list a list of simulated standardized score processes under the null hypothesis.

  • max_abs_value the supremum of the absolute value of the observed standardized score process for each covariate and the supremum of the sum of absolute values of the observed standardized score processes across all covariates.

  • p_value the p-values for the supremum tests for each covariate and the global test.

Details

The supremum test corresponds to the ASSESS statement with ph option of SAS PROC PHREG.

References

D. Y. Lin, L. J. Wei, and Z. Ying. Checking the Cox model with cumulative sums of martingale-based residuals. Biometrika 1993; 80:557-572.

Author

Kaifeng Lu, kaifenglu@gmail.com

Examples


fit <- phregr(data = liver, time = "Time", event = "Status",
              covariates = c("log(Bilirubin)", "log(Protime)",
                             "log(Albumin)", "Age", "Edema"),
              ties = "breslow")

aph <- assess_phregr(fit, resample = 1000, seed = 314159)

aph
#>        covariate max_abs_value resample   seed p_value
#> 1 log(Bilirubin)        1.0880     1000 314159  0.1220
#> 2   log(Protime)        1.7243     1000 314159  <.0001
#> 3   log(Albumin)        0.8443     1000 314159  0.4490
#> 4            Age        0.7387     1000 314159  0.4970
#> 5          Edema        1.4350     1000 314159  0.0260
#> 6         GLOBAL        4.5107     1000 314159  0.0080

plot(aph, nsim = 20)
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