Principal causal effect identification and surrogate end point evaluation by multiple trials

• 成果类型: Article
• 署名作者: Jiang, Zhichao; Ding, Peng; Geng, Zhi
• 署名单位: Peking University; University of California System; University of California Berkeley
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12135
• 发表日期: 2016
• 页码: 829-848
• 关键词: potential outcomes; augmented designs; vaccine trials; stratification; inference; noncompliance; criteria; models; death; distributions

摘要

Principal stratification is a causal framework to analyse randomized experiments with a post-treatment variable between the treatment and end point variables. Because the principal strata defined by the potential outcomes of the post-treatment variable are not observable, we generally cannot identify the causal effects within principal strata. Motivated by a real data set of phase III adjuvant colon cancer clinical trials, we propose approaches to identifying and estimating the principal causal effects via multiple trials. For the identifiability, we remove the commonly used exclusion restriction assumption by stipulating that the principal causal effects are homogeneous across these trials. To remove another commonly used monotonicity assumption, we give a necessary condition for the local identifiability, which requires at least three trials. Applying our approaches to the data from adjuvant colon cancer clinical trials, we find that the commonly used monotonicity assumption is untenable, and disease-free survival with 3-year follow-up is a valid surrogate end point for overall survival with 5-year follow-up, which satisfies both causal necessity and causal sufficiency. We also propose a sensitivity analysis approach based on Bayesian hierarchical models to investigate the effect of the deviation from the homogeneity assumption.