Efficient nonparametric estimation of causal effects in randomized trials with noncompliance

Jing Cheng, Dylan S. Small, Zhiqiang Tan and Thomas R. Ten Have

Biometrika, 2009, vol. 96, issue 1, 19-36

Abstract: 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. Copyright 2009, Oxford University Press.

Date: 2009
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