Toward Personalizing Care: Assessing Heterogeneity of Treatment Effects in Randomized Trials (2024)

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    Editorial

    March 21, 2023

    Issa J.Dahabreh,MD, ScD1,2,3; Dhruv S.Kazi,MD, MS3,4

    Author Affiliations Article Information

    • 1CAUSALab and Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts

    • 2Statistical Editor, JAMA

    • 3Richard A. and Susan F. Smith Center for Outcomes Research, Beth Israel Deaconess Medical Center, Boston, Massachusetts

    • 4Harvard Medical School, Boston, Massachusetts

    JAMA. 2023;329(13):1063-1065. doi:10.1001/jama.2023.3576

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    Clinicians know that individual patients may respond differently to a given treatment and that the overall treatment effect reported in a randomized trial of the treatment may not be directly applicable to all patients in clinical practice.1 Determining the treatment effect for an individual patient involves a comparison of the outcome when that patient is exposed to the treatment vs the outcome of the same patient exposed to a control treatment at the same time, a comparison impossible to make in conventional parallel-group trial designs. A practical alternative is to examine heterogeneity of (variation in) treatment effects across groups of patients, categorized by baseline demographic or clinical characteristics, such as age or risk factors for the outcome.2

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    Dahabreh IJ, Kazi DS. Toward Personalizing Care: Assessing Heterogeneity of Treatment Effects in Randomized Trials. JAMA. 2023;329(13):1063–1065. doi:10.1001/jama.2023.3576

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