Random effects Cox models: A Poisson modelling approach

• 成果类型: Article
• 署名作者: Ma, RJ; Krewski, D; Burnett, RT
• 署名单位: University of New Brunswick; University of Ottawa; Health Canada
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/90.1.157
• 发表日期: 2003
• 页码: 157169
• 关键词: air-pollution; survival analysis; longitudinal data; reml estimation; frailty; mortality; association; regression; inference

摘要

We propose a Poisson modelling approach to nested random effects Cox proportional hazards models. An important feature of this approach is that the principal results depend only on the first and second moments of the unobserved random effects. The orthodox best linear unbiased predictor approach to random effects Poisson modelling techniques enables us to justify appropriate consistency and optimality. The explicit expressions for the random effects given by our approach facilitate incorporation of a relatively large number of random effects. The use of the proposed-methods is illustrated through the reanalysis of data from a large-scale cohort study of particulate air pollution and mortality previously reported by Pope et al. (1995).