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A tool for empirical equipoise assessment in multigroup comparative effectiveness research.

Kazuki Yoshida1,2,3, Daniel H Solomon1,4, Sebastien Haneuse3

  • 1Division of Rheumatology, Immunology and Allergy, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.

Pharmacoepidemiology and Drug Safety
|May 28, 2019
PubMed
Summary

This study extends empirical equipoise assessment for multigroup observational studies, offering a tool to evaluate patient similarity and guide cohort identification for valid inference.

Keywords:
multigroup comparative effectivenessmultinomial exposurepharmacoepidemiologypropensity score

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Services Research

Background:

  • Ensuring patient similarity between groups is crucial for valid inference in observational research.
  • Empirical equipoise, based on propensity scores (PS), was previously developed to assess this similarity.
  • Existing methods were limited to two-group comparisons.

Purpose of the Study:

  • To extend the empirical equipoise tool for multigroup observational studies.
  • To develop a method for assessing patient similarity in studies with more than two comparison groups.
  • To evaluate the performance of the extended tool in the presence of residual confounding.

Main Methods:

  • Modified the empirical equipoise tool to accommodate multinomial exposures.
  • Applied the tool to assess study design in three-group clinical examples.
  • Conducted three-group simulations to evaluate performance under residual confounding after PS weighting.

Main Results:

  • In a rheumatoid arthritis example, empirical equipoise improved for second-line biologics (57.7%) compared to first-line (44.5%).
  • Simulations showed the tool identified residual confounding at 20% when unmeasured confounder effects matched measured ones.
  • Sensitivity decreased with stronger unmeasured confounder effects on treatment, identifying confounding at 30%.

Conclusions:

  • The proposed empirical equipoise tool aids in guiding cohort identification for multigroup observational studies.
  • It is particularly useful when unmeasured and measured covariates have similar effects on treatment and outcomes.
  • The tool helps ensure the validity of inference in complex comparative studies.