Assessing the Effect of an Influenza Vaccine in an Encouragement Design

by Keisuke Hirano (1), Guido W. Imbens (1), Donald B. Rubin (2) and Xiao-Hua Zhou (3)
(1) Department of Economics, University of California, Los Angeles, CA 90095, USA

(2) Department of Statistics, Science Center 709, Harvard University, Cambridge, MA 02138, USA

(3) Division of Biostatistics, Department of Medicine, University School of Medicine and Regenstrief Institute for Health Care, Indiana, Indianapolis, IN 46202, USA

Abstract
Many randomized experiments suffer from noncompliance. Some of these experiments, so-called encouragement designs, can be expected to have especially large amounts of noncompliance, because encouragement to take the treatment rather than the treatment itself is randomly assigned to individuals. We present an extended framework for the analysis of data from such experiments with a binary treatment, binary encouragement, and background covariates. There are two key features of this framework: we use an instrumental variables approach to link intention-to-treat effects to treatment effects and we adopt a Bayesian approach for inference and sensitivity analysis. This framework is illustrated in a medical example concerning the effects of inoculation for influenza. In this example, the analyses suggest that positive estimates of the intention-to-treat effect need not be due to the treatment itself, but rather to the encouragement to take the treatment: the intention-to-treat effect for the subpopulation who would be inoculated whether or not encouraged is estimated to be approximately as large as the intention-to-treat effect for the subpopulation whose inoculation status would agree with their (randomized) encouragement status whether or not encouraged. Thus, our methods suggest that global intention-to-treat estimates, although often regarded as conservative, can be too coarse and even misleading when taken as summarizing the evidence in the data for the effects of treatments.

Keywords: Bayesian analysis; Causal inference; Instrumental variables; Noncompliance; Rubin Causal Model; Potential outcomes; Treatment effects; Sensitivity analysis

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