Estimating the effect of treatment in a proportional hazards model in the presence of non-compliance and contamination

• 成果类型: Article
• 署名作者: Cuzick, Jack; Sasieni, Peter; Myles, Jonathan; Tyrer, Jonathan
• 署名单位: Cancer Research UK; University of London; Queen Mary University London
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2007.00600.x
• 发表日期: 2007
• 页码: 565-588
• 关键词: clinical-trials; DESIGN

摘要

Methods for adjusting for non-compliance and contamination, which respect the randomization, are extended from binary outcomes to time-to-event analyses by using a proportional hazards model. A simple non-iterative method is developed when there are no covariates, which is a generalization of the Mantel-Haenszel estimator. More generally, a 'partial likelihood' is developed which accommodates covariates under the assumption that they are independent of compliance. A key feature is that the proportion of contaminators and non-compliers in the risk set is updated at each failure time. When covariates are not independent of compliance, a full likelihood is developed and explored, but this leads to a complex estimator. Estimating equations and information matrices are derived for these estimators and they are evaluated by simulation studies.