Causal inference for community‐based multi‐layered intervention study |
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Authors: | Pan Wu Douglas Gunzler Naiji Lu Tian Chen Peter Wymen Xin M Tu |
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Institution: | 1. Value Institute, Christiana Care Health System, , Newark, DE 19718 U.S.A.;2. Center for Health Care Research & Policy, Case Western Reserve University at MetroHealth Medical Center, , Cleveland, OH 44109‐1998, U.S.A.;3. Department of Biostatistics and Computational Biology, University of Rochester, , Rochester, NY 14642, U.S.A.;4. Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, , Canandaigua, NY 14424, U.S.A.;5. Department of Psychiatry, University of Rochester, , Rochester, NY 14642, U.S.A. |
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Abstract: | Estimating causal treatment effect for randomized controlled trials under post‐treatment confounding, that is, noncompliance and informative dropouts, is becoming an important problem in intervention/prevention studies when the treatment exposures are not completely controlled. When confounding is present in a study, the traditional intention‐to‐treat approach could underestimate the treatment effect because of insufficient exposure of treatment. In the recent two decades, many papers have been published to address such confounders to investigate the causal relationship between treatment and outcome of interest based on different modeling strategies. Most of the existing approaches, however, are suitable only for standard experiments. In this paper, we propose a new class of structural functional response model to address post‐treatment confounding in complex multi‐layered intervention studies within a longitudinal data setting. The new approach offers robust inference and is readily implemented. We illustrate and assess the performance of the proposed structural functional response model using both real and simulated data. Copyright © 2014 John Wiley & Sons, Ltd. |
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Keywords: | causal treatment effect noncompliance functional response models randomized controlled trials missing data |
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