Efficient nonparametric estimation of causal effects in randomized trials with noncompliance

• 成果类型: Article
• 署名作者: Cheng, Jing; Small, Dylan S.; Tan, Zhiqiang; Ten Have, Thomas R.
• 署名单位: State University System of Florida; University of Florida; University of Pennsylvania; Rutgers University System; Rutgers University New Brunswick; University of Pennsylvania
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asn056
• 发表日期: 2009
• 页码: 1936
• 关键词: empirical likelihood; convergence properties; confidence-intervals; identification; inference; efficacy

摘要

Causal approaches based on the potential outcome framework provide a useful tool for addressing noncompliance problems in randomized trials. We propose a new estimator of causal treatment effects in randomized clinical trials with noncompliance. We use the empirical likelihood approach to construct a profile random sieve likelihood and take into account the mixture structure in outcome distributions, so that our estimator is robust to parametric distribution assumptions and provides substantial finite-sample efficiency gains over the standard instrumental variable estimator. Our estimator is asymptotically equivalent to the standard instrumental variable estimator, and it can be applied to outcome variables with a continuous, ordinal or binary scale. We apply our method to data from a randomized trial of an intervention to improve the treatment of depression among depressed elderly patients in primary care practices.