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A Causal Model for Joint Evaluation of Placebo and Treatment-Specific Effects in Clinical Trials

Authors

  • Zhiwei Zhang,

    Corresponding author
    1. Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland 20993, U.S.A.
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  • Richard M. Kotz,

    1. Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland 20993, U.S.A.
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  • Chenguang Wang,

    1. Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, U.S.A.
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  • Shiling Ruan,

    1. Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland 20993, U.S.A.
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  • Martin Ho

    1. Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland 20993, U.S.A.
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Abstract

Summary

Evaluation of medical treatments is frequently complicated by the presence of substantial placebo effects, especially on relatively subjective endpoints, and the standard solution to this problem is a randomized, double-blinded, placebo-controlled clinical trial. However, effective blinding does not guarantee that all patients have the same belief or mentality about which treatment they have received (or treatmentality, for brevity), making it difficult to interpret the usual intent-to-treat effect as a causal effect. We discuss the causal relationships among treatment, treatmentality and the clinical outcome of interest, and propose a causal model for joint evaluation of placebo and treatment-specific effects. The model highlights the importance of measuring and incorporating patient treatmentality and suggests that each treatment group should be considered a separate observational study with a patient's treatmentality playing the role of an uncontrolled exposure. This perspective allows us to adapt existing methods for dealing with confounding to joint estimation of placebo and treatment-specific effects using measured treatmentality data, commonly known as blinding assessment data. We first apply this approach to the most common type of blinding assessment data, which is categorical, and illustrate the methods using an example from asthma. We then propose that blinding assessment data can be collected as a continuous variable, specifically when a patient's treatmentality is measured as a subjective probability, and describe analytic methods for that case.

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