A model checking method for the proportional hazards model with recurrent gap time data

Chiung Yu Huang, Xianghua Luo, Dean A. Follmann

Research output: Contribution to journalArticle

Abstract

Recurrent events are the natural outcome in many medical and epidemiology studies. To assess covariate effects on the gaps between consecutive recurrent events, the Cox proportional hazards model is frequently employed in data analysis. The validity of statistical inference, however, depends on the appropriateness of the Cox model. In this paper, we propose a class of graphical techniques and formal tests for checking the Cox model with recurrent gap time data. The building block of our model checking method is an averaged martingale-like process, based on which a class of multiparameter stochastic processes is proposed. This maneuver is very general and can be used to assess different aspects of model fit. Numerical simulations are conducted to examine finite-sample performance, and the proposed model checking techniques are illustrated with data from the Danish Psychiatric Central Register.

Original languageEnglish (US)
Pages (from-to)535-547
Number of pages13
JournalBiostatistics
Volume12
Issue number3
DOIs
StatePublished - Jul 1 2011

Keywords

  • Correlated failure times
  • Induced-dependent censoring
  • Kaplan-Meier estimator
  • Renewal processes

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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