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Semi‐parametric estimation of covariate effects using the positive stable frailty model

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  • S. T. Wang
  • John P. Klein
  • M. L. Moeschberger

Abstract

Many biological and medical studies have as a response of interest the time to occurrence of some event, such as the occurrence of a particular symptom or disease, remission, relapse, death due to some specific disease, or simply death. In this paper we study the problem of assessing the effect of potential risk factors on the outcome event of interest through a parametric or semi‐parametric frailty model where the lifetimes have a reason to be considered dependent. This dependence may arise because of multiple endpoints within the same individual or because, when studying a single endpoint, there are natural groupings between study subjects. The objective of this paper is to extend both parametric and semi‐parametric approaches to regression analysis in which the lifetimes of individuals in a group are effected by the same random frailty which follows a positive stable distribution. Some comparisons of the properties of this frailty distribution with other frailty distributions are made and an example which assesses the effect of a treatment in a litter‐matched tumorigenesis study is presented.

Suggested Citation

  • S. T. Wang & John P. Klein & M. L. Moeschberger, 1995. "Semi‐parametric estimation of covariate effects using the positive stable frailty model," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 11(2), pages 121-133, June.
    • Handle: RePEc:wly:apsmda:v:11:y:1995:i:2:p:121-133
    DOI: 10.1002/asm.3150110203
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    Cited by:

    1. John P. Klein & Corey Pelz & Mei-jie Zhang, 1999. "Modeling Random Effects for Censored Data by a Multivariate Normal Regression Model," Biometrics, The International Biometric Society, vol. 55(2), pages 497-506, June.

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