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Handling missing data in propensity score estimation in comparative effectiveness evaluations: a systematic review.

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This summary is machine-generated.

Most studies on comparative effectiveness use incomplete methods for missing data, potentially biasing results. Researchers rarely report their missing data handling, hindering transparency in propensity score methods.

Keywords:
comparative effectivenessmissing datapropensity score

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Area of Science:

  • Health Services Research
  • Biostatistics
  • Epidemiology

Background:

  • Propensity score methods are crucial for observational comparative effectiveness studies.
  • Missing data present a significant challenge in these studies.
  • Existing reviews focus on propensity score application, not missing data handling.

Purpose of the Study:

  • To review how missing data are addressed using propensity score methods.
  • To assess the prevalence of appropriate missing data techniques in the literature.

Main Methods:

  • Systematic review of observational comparative effectiveness studies.
  • Searched MEDLINE and EMBASE databases for relevant articles.
  • Analyzed 167 eligible studies regarding their handling of missing data.

Main Results:

  • Complete case analysis was used in 68% of studies, often unreported.
  • Only 16 studies reported using multiple imputation.
  • A significant majority of studies failed to adequately address missing data.

Conclusions:

  • Few researchers employ robust methods for missing data in propensity score analyses.
  • Inadequate reporting of missing data methodology can lead to biased findings.
  • Improved transparency and methodology are needed for missing data in comparative effectiveness research.