Censored linear regression for case-cohort studies

• 成果类型: Article
• 署名作者: Nan, Bin; Yu, Menggang; Kalbfleisch, John D.
• 署名单位: University of Michigan System; University of Michigan; Indiana University System; Indiana University Indianapolis; University of Michigan System; University of Michigan
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/93.4.747
• 发表日期: 2006
• 页码: 747762
• 关键词: national wilms-tumor; models

摘要

Right-censored data from a classical case-cohort design and a stratified case-cohort design are considered. In the classical case-cohort design the subcohort is obtained as a simple random sample of the entire cohort, whereas in the stratified design this subcohort is elected by independent Bernoulli sampling with arbitrary selection probabilities. For each design and under a linear regression model, methods for estimating the regression parameters are proposed and analysed. These methods are derived by modifying the linear ranks tests and estimating equations that arise from full-cohort data using methods that are similar to the pseudolikelihood estimating equation that has been used in relative risk regression for these models. The estimators so obtained are shown to be consistent and asymptotically normal. Variance estimation and numerical illustrations are also provided.