Estimation of Causal Effect on Treatment on Longitudinal Binary Outcomes in Randomized Clinical Trials with Non-compliance
In this paper, we focus on estimating the complier average causal effect for longitudinal binary outcomes in randomized clinical trials with non-compliance. Sato3 proposed a simple addictive risk model for repeated binary outcomes in randomized clinical trials with non-compliance and extended the instrumental variable estimator to repeated binary outcomes based on a randomization-based approach. Matuyama5 extended Satos result to a general case of repeated binary data. Sato3 and Matuyama5 gave the point and interval estimators for the risk difference. Lui6 derived variances of these point estimators. But all of these results were derived under the very strong addictive risk model assumption. In order to relax the assumption, we focus on a special case of Satos; that is, the treatments patients received at the different time points are the same. We proposed the potential outcomes model, defined the parameters of average causal effects in Rubins causal models of three repeated binary outcomes, and discussed parameter identifiability and derived the simple ML likelihood (ML) estimators of average causal effects in different time points under stable unit treatment values, monotonicity and exclusion restriction assumptions. Then we conducted simulation study to evaluate the finite-sample performance for the proposed ML estimators.
Non-compliance Average Causal Effect Repeated Section Identification
LI Xiaotong
Department of Mathematics and Physics, China University of Petroleum, Beijing, P.R.China, 102249
国际会议
2008年国际应用统计学术研讨会(2008 International Institute of Applied Statistics Studies)
烟台
英文
1-7
2008-08-14（万方平台首次上网日期，不代表论文的发表时间）