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Causal Models for Randomized Trials with Continuous Compliance

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Statistical Causal Inferences and Their Applications in Public Health Research

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

Abstract

In behavioral medicine trials, such as smoking cessation trials, 2 or more active treatments are often compared. Noncompliance by some subjects with their assigned treatment poses a challenge to the data analyst. In particular, the joint distribution of the observed and counterfactual compliance variables cannot be identified, without imposing strong assumptions. However, due to randomization, each marginal compliance distribution can be identified. These marginal distributions impose bounds on the joint distribution. Our approach is to use a copula model to link the two marginal distributions, up to a sensitivity parameter. We then take a principal stratification approach to estimate causal effects. We develop this approach when compliance is either binary (yes/no) or continuous (dose).

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Author information

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, The George Washington University, Washington, DC, USA

    Yan Ma

  2. Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA

    Jason Roy

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  1. Yan Ma
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  2. Jason Roy
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Correspondence to Yan Ma .

Editor information

Editors and Affiliations

  1. Department of Epidemiology School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA

    Hua He

  2. Christiana Care Health System, Value Institute, Newark, Delaware, USA

    Pan Wu

  3. School of Social Work and Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA

    Ding-Geng (Din) Chen

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Ma, Y., Roy, J. (2016). Causal Models for Randomized Trials with Continuous Compliance. In: He, H., Wu, P., Chen, DG. (eds) Statistical Causal Inferences and Their Applications in Public Health Research. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-41259-7_10

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