Faculty Collaboration Database

Asymptotic theory for the Cox semi-Markov illness-death model. Lifetime Data Anal 2007 Mar;13(1):91-117

Date

11/23/2006

Pubmed ID

17115258

DOI

10.1007/s10985-006-9018-9

Scopus ID

2-s2.0-33847298638 (requires institutional sign-in at Scopus site)   13 Citations

Abstract

Irreversible illness-death models are used to model disease processes and in cancer studies to model disease recovery. In most applications, a Markov model is assumed for the multistate model. When there are covariates, a Cox (1972, J Roy Stat Soc Ser B 34:187-220) model is used to model the effect of covariates on each transition intensity. Andersen et al. (2000, Stat Med 19:587-599) proposed a Cox semi-Markov model for this problem. In this paper, we study the large sample theory for that model and provide the asymptotic variances of various probabilities of interest. A Monte Carlo study is conducted to investigate the robustness and efficiency of Markov/Semi-Markov estimators. A real data example from the PROVA (1991, Hepatology 14:1016-1024) trial is used to illustrate the theory.

Author List

Shu Y, Klein JP, Zhang MJ

Author

Mei-Jie Zhang PhD Professor in the Institute for Health and Equity department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Biometry
Computer Simulation
Humans
Likelihood Functions
Markov Chains
Neoplasms
Prevalence
Proportional Hazards Models
Survival Analysis