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Activity Number: 618
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 2:00 PM to 3:50 PM
Sponsor: Mental Health Statistics Section
Abstract #319944 View Presentation
Title: Design, Identification, and Sensitivity Analysis for Patient Preference Trials
Author(s): Teppei Yamamoto* and Dean Knox and Berinsky Adam and Matthew Baum
Companies: MIT and MIT and MIT and Harvard
Keywords: randomized controlled trials ; external validity ; causal inference ; experimental design ; nonparametric bounds ; stated and revealed preferences
Abstract:

Social and medical scientists are often concerned that the external validity of experimental results may be compromised because of heterogeneous treatment effects between the subjects who would choose to take it and who would not in the real world. Patient preference trials (PPTs), where participants' preferences over treatment options are incorporated in the study design, provide a possible solution. In this paper, we provide a systematic analysis of PPTs based on the potential outcomes framework of causal inference. We propose a general design for PPTs with multi-valued treatments, where participants state their preferred treatments and are then randomized into either a standard randomized experiment or a self-selection condition. We derive nonparametric sharp bounds on the average causal effects among each choice-based subpopulation of participants and propose a sensitivity analysis for the violation of the key ignorability assumption. The proposed design and methodology are illustrated with an original study of partisan news media and its behavioral impact.


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