Methods for the analysis of sampled cohort data in the Cox proportional hazards model

• 成果类型: Article
• 署名作者: Borgan, O; Goldstein, L; Langholz, B
• 署名单位: University of Southern California; University of Southern California
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176324322
• 发表日期: 1995
• 页码: 1749-1778
• 关键词: nested case-control; REGRESSION-MODEL; BIASED SELECTION

摘要

Methods are provided for regression parameter and cumulative baseline hazard estimation in the Cox proportional hazards model when the cohort is sampled according to a predictable sampling probability law. The key to the methodology is to define counting processes which count joint failure and sampled risk sets occurrences and to derive the appropriate intensities for these counting processes, leading to estimation methods parallel to those for full cohort data. These techniques are illustrated for a number of sampling designs, including three novel designs: counter-matching with additional randomly sampled controls; quota sampling of controls; and nested case-control sampling with number of controls dependent on the failure's exposure status. General asymptotic theory is developed for the maximum partial likelihood estimator and cumulative baseline hazard estimator and is used to derive the asymptotic distributions for estimators from a large class of designs.