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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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An imputation-based solution to using mismeasured covariates in propensity score analysis.

Yenny Webb-Vargas1, Kara E Rudolph2,3,4, David Lenis1

  • 11 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.

Statistical Methods in Medical Research
|June 4, 2015
PubMed
Summary

Covariate measurement error can bias treatment effect estimates in propensity score methods. A multiple imputation for external calibration approach effectively corrects this bias when a calibration dataset is available.

Keywords:
Causal inferencemeasurement errormultiple imputationnonexperimental study

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical Methods

Background:

  • Covariate measurement error is common in observational studies.
  • Existing methods for handling this error in propensity score analysis are limited.

Purpose of the Study:

  • To investigate a multiple imputation-based approach for correcting covariate measurement error in propensity score methods.
  • To evaluate the performance of this method using simulation studies and a real-world example.

Main Methods:

  • Utilized multiple imputation for external calibration (MI-EC) using a calibration sample with true and mismeasured covariates.
  • Conducted simulation studies to assess bias reduction.
  • Applied the MI-EC approach to estimate the effects of neighborhood disadvantage on adolescent mental health.

Main Results:

  • Using mismeasured covariates in propensity score estimation introduced significant bias in treatment effect estimates.
  • The MI-EC method successfully eliminated almost all bias.
  • Incorporating the outcome variable into the imputation process was crucial for accurate results.

Conclusions:

  • Covariate measurement error poses a substantial challenge in propensity score analysis.
  • Multiple imputation for external calibration offers a robust solution for correcting such bias.
  • The MI-EC method is a valuable tool for improving the validity of causal inference from observational data.