A simulation-based goodness-of-fit test for survival data

Gang Li and Yanqing Sun

Statistics & Probability Letters, 2000, vol. 47, issue 4, 403-410

Abstract: To check the validity of a parametric model for survival data, a number of supremum-type tests have been proposed in the literature using Khmaladze's (1993, Ann. Statist. 18, 582-602) transformation of a test process. However, such a transformation is usually very complicated and lacks a clear interpretation. Information could also be lost through transformation. In this note, we propose a simulation-based supremum-type test directly from the original test process using an idea originally introduced by Lin et al. (1993, Biometrika 80, 557-572). The test is developed under the framework of Aalen's (1978, Ann. Statist. 6, 701-726) multiplicative intensity counting process model, and therefore applies to a number of survival models including those with very general forms of censoring and truncation. By comparing the observed test process with a set of simulated realizations of an approximating process, our method can be used as a graphical tool as well as a formal test for checking the adequacy of the assumed parametric model. We establish consistency of the resulting test under any fixed alternative. Its performance is investigated in a simulation study. Illustrations are given using some real data sets.

Keywords: Censoring; Kolmogorov-Smirnov; test; Monte; Carlo; method; Product-limit; estimator; Truncation; Weak; convergence (search for similar items in EconPapers)
Date: 2000
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Citations: View citations in EconPapers (2)

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