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Mini-Sentinel: Statistical Methods for Improving Confounder Adjustment For Emergent Treatment Comparison

    Basic Details
    Date Posted

    This report describes a sequential framework for monitoring newly marketed treatments while balancing measured confounders, and then uses this framework to guide decisions on selecting optimal confounder adjustment methods.  The report applies the framework to monitor the safety of newly marketed molecular entities using the Mini-Sentinel Distributed Database.  It also provides codes for measuring the strength of the association between treatment and confounders, calculating time-varying Disease Risk Scores (DRS) and Propensity Scores (PS), score matching, stratification, and sequential analyses.

    Data Source(s)
    Mini-Sentinel Distributed Database (MSDD)
    Workgroup Leader(s)

    Stanley Xu, PhD; The Institute for Health Research, Kaiser Permanente Colorado, Denver, CO

    Workgroup Member(s)

    Susan Shetterly, MS; Marsha A. Raebel, PharmD; The Institute for Health Research, Kaiser Permanente Colorado, Denver, CO

    Andrea J. Cook, PhD; Biostatistics Unit, Group Health Research Institute, Seattle, WA

    Sunali Goonesekera, MS; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA

    Azadeh Shoaibi, MS, MHS; Eric Frimpong, PhD; Brad McEvoy, PhD; Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD

    Jason Roy, PhD; Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA

    Bruce Fireman, MS, Division of Research, Kaiser Permanente Northern California, Oakland, CA