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Developments in post-marketing comparative effectiveness research.

S Schneeweiss1

  • 1Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. schneeweiss@post.harvard.edu

Clinical Pharmacology and Therapeutics
|June 8, 2007
PubMed
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Comparative effectiveness research compares treatments in real-world settings. Advanced statistical methods using healthcare databases help overcome challenges in analyzing drug effectiveness for better clinical decisions.

Area of Science:

  • Health Services Research
  • Pharmacoeconomics
  • Clinical Epidemiology

Background:

  • Physicians and insurers require comparative data on new versus existing drugs for treatment and formulary decisions.
  • Drug manufacturers have limited incentive for head-to-head trials, as regulatory approval (e.g., by the Food and Drug Administration (FDA)) typically involves placebo comparisons.
  • Comparative effectiveness research (CER) aims to fill this gap by directly comparing treatments in routine clinical practice.

Purpose of the Study:

  • To explore the utility of health-care utilization databases for comparative effectiveness research.
  • To identify and discuss methods for addressing confounding in non-randomized database studies.

Main Methods:

  • Utilizing large-scale health-care utilization databases that capture drug use and outcomes in routine care.

Related Experiment Videos

  • Employing advanced statistical techniques to mitigate confounding, including high-dimensional propensity scores, crossover designs analyzing within-patient variation, and instrumental variable (IV) analyses leveraging between-provider prescribing differences.

    • Main Results:

    • Health-care databases offer a timely reflection of routine care, making them a potentially preferred data source for CER.
    • Recent methodological developments show promise in enhancing the validity of non-randomized comparative effectiveness studies.
    • Specific methods like proxy variables, within-patient variation, and instrumental variables can address biases inherent in observational data.

    Conclusions:

    • Health-care utilization databases are valuable resources for comparative effectiveness research.
    • Advanced statistical methods are crucial for overcoming confounding and ensuring the validity of findings from these databases.
    • These advancements bring the field closer to providing reliable head-to-head treatment comparisons for clinical decision-making.