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Propensity score matching after multiple imputation when a confounder has missing data.

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Statistics in Medicine
|January 25, 2023
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This summary is machine-generated.

Combining multiple imputation and propensity score matching for causal inference can inflate confidence intervals. A proposed correction to Rubin

Keywords:
confoundingmissing datamultiple imputationpropensity score matching

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical Inference

Background:

  • Confounding is a major challenge in causal inference using observational data.
  • Propensity score methods, including propensity score matching, are standard for addressing confounding.
  • Multiple imputation is commonly used to handle missing data in statistical analyses.

Purpose of the Study:

  • To investigate the impact of combining multiple imputation with propensity score matching.
  • To identify and explain the cause of over-coverage in confidence intervals when these methods are combined.
  • To evaluate a proposed correction to Rubin's rules for multiple imputation in this context.

Main Methods:

  • Propensity score matching was applied to observational data.
  • Multiple imputation was used to handle missing outcome or covariate data.
  • A correction to Rubin's rules was evaluated for its performance in mitigating over-coverage.

Main Results:

  • The combination of multiple imputation and propensity score matching leads to over-coverage of confidence intervals for treatment effect estimates.
  • The study identifies the underlying cause of this over-coverage.
  • The evaluated correction to Rubin's rules successfully removes the observed over-coverage.

Conclusions:

  • Standard application of multiple imputation with propensity score matching can yield overly wide confidence intervals.
  • A specific correction to Rubin's rules effectively addresses this issue.
  • This correction improves the reliability of causal effect estimates derived from observational data with missingness.