Application of marginal structural models in pharmacoepidemiologic studies: a systematic review

Authors

  • Shibing Yang,

    1. Department of Family Medicine and Population Health, Division of Epidemiology, Virginia Commonwealth University, Richmond, VA, USA
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  • Charles B. Eaton,

    1. Center for Primary Care and Prevention, Memorial Hospital of Rhode Island, Pawtucket, RI, USA
    2. Department of Family Medicine, Warren Alpert Medical School, Brown University, Providence, RI, USA
    3. Department of Epidemiology, Warren Alpert Medical School, Brown University, Providence, RI, USA
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  • Juan Lu,

    1. Department of Family Medicine and Population Health, Division of Epidemiology, Virginia Commonwealth University, Richmond, VA, USA
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  • Kate L. Lapane

    Corresponding author
    1. Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA
    • Correspondence to: K. L. Lapane, Department of Quantitative Health Sciences, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655-0002, USA.

      E-mail: kate.lapane@umassmed.edu

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ABSTRACT

Purpose

We systematically reviewed pharmacoepidemiologic studies published in 2012 that used inverse probability weighted (IPW) estimation of marginal structural models (MSM) to estimate the effect from a time-varying treatment.

Methods

Potential studies were retrieved through a citation search within Web of Science and a keyword search within PubMed. Eligibility of retrieved studies was independently assessed by at least two reviewers. One reviewer performed data extraction, and a senior epidemiologist confirmed the extracted information for all eligible studies.

Results

Twenty pharmacoepidemiologic studies were eligible for data extraction. The majority of reviewed studies did not report whether the positivity assumption was checked. Six studies performed intention-to-treat analyses, but none of them reported adherence levels after treatment initiation. Eight studies chose an as-treated analytic strategy, but only one of them reported modeling the multiphase of treatment use. Almost all studies performing as-treated analyses chose the most recent treatment status as the functional form of exposure in the outcome model. Nearly half of the studies reported that the IPW estimate was substantially different from the estimate derived from a standard regression model.

Conclusions

The use of IPW method to control for time-varying confounding is increasing in medical literature. However, reporting of the application of the technique is variable and suboptimal. It may be prudent to develop best practices in reporting complex methods in epidemiologic research. Copyright © 2014 John Wiley & Sons, Ltd.

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