Statistics Papers

Document Type

Journal Article

Date of this Version

3-2009

Publication Source

Biometrika

Volume

96

Issue

1

Start Page

19

Last Page

36

DOI

10.1093/biomet/asn056

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/Permission Statement

This is a post-peer-review, pre-copyedit version of an article published in Biometrika.

Keywords

causal effect, efficient nonparametric estimation, empirical likelihood, instrumental variable, noncompliance, randomized trial

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Date Posted: 27 November 2017

This document has been peer reviewed.