MSCCT (version 1.0.2)

multi_ts: Two-staged test for comparison of two or more survival curves.

Description

Performs a two-stage test for each pair of survival curves and apply a correction in case of several comparisons.

Usage

multi_ts(df, method = p.adjust.methods, eps = 0.1, nboot = 100)

Value

An object of class multi_ts containing:

• results : A matrix. Each row represents a comparison of two curves and contains the p-values for both stage, the global p-value and the adjusted global p-value;

• p : The p-value for the global test;

• nb_tests : The number of performed Two-Stage tests;

• the parameters eps, method and nboot.

Arguments

df

A dataframe with columns :

• time : positive numbers, time-to-event;

• status : vector of integer, 0 or 1. 0 is (right) censoring, 1 is event;

• arm : a factor or object that can be coerced to one. The group the patient belongs to. Must have at least two levels.

method

The correction used for the p-values. Must be in p.adjust.methods. Default is the Holm correction. Unused if number of groups equals two.

eps

A number such that 0 < eps < 0.5. See reference for more information;

nboot

A positive integer, number of bootstrap sample for the second stage;

Details

For a two-stage test, the first stage is a log-rank test. If the first test is significant, then the whole procedure stops and we conclude that the survival curves are different. If it is not significant, then the survival curves are either equal or crossing each other and the log-rank test can't conclude the difference. A second test is performed to distinguish these two cases.

For multiple curves comparison, the two-stage test is a pairwise test. A two-stage test is performed for each pair of curves.

References

• Qiu, P., & Sheng, J. (2008). A two-stage procedure for comparing hazard rate functions. Journal of the Royal Statistical Society Series B: Statistical Methodology, 70(1), 191-208. Chen, Zhongxue & Huang, Hanwen & Qiu, Peihua. (2017).

• Chen, Z., Huang, H., & Qiu, P. (2017). An improved two-stage procedure to compare hazard curves. Journal of Statistical Computation and Simulation, 87(9), 1877-1886.

Examples

  # test with a quarter of the data frame data_not_PH
  ind = c(1:100, 401:500, 801:900)
  multi_ts(data_not_PH[ind,], method = "BH", eps = 0.1, nboot = 10)