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Now trending: Coping with non-parallel trends in difference-in-differences analysis.

Andrew M Ryan1, Evangelos Kontopantelis2, Ariel Linden3

  • 1Department of Health Management and Policy, School of Public Health, University of Michigan, Ann Arbor, MI, USA.

Statistical Methods in Medical Research
|November 27, 2018
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Summary
This summary is machine-generated.

When parallel trends fail in difference-in-differences (DID) analysis, DID with propensity score matching offers more accurate causal effect estimates than standard DID or interrupted time-series analysis.

Keywords:
Difference-in-differencesMonte Carlo simulationhealth policyhealth services researchnon-parallel trends

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Area of Science:

  • Health Services Research
  • Econometrics
  • Biostatistics

Background:

  • Difference-in-differences (DID) analysis is a common method for evaluating health policies.
  • A key assumption is parallel trends, meaning outcomes would have followed similar paths in treated and control groups without the intervention.
  • Guidance is limited for situations where this parallel trends assumption is violated.

Purpose of the Study:

  • To assess the performance of various estimators when the parallel trends assumption is violated in DID analysis.
  • To compare standard DID, DID with propensity score matching, and interrupted time-series analyses under violated parallel trends.

Main Methods:

  • A Monte Carlo simulation experiment was conducted.
  • Nationwide US hospital data (n=3737) across seven periods were used.
  • Placebo interventions evaluated clinical process quality and mortality for AMI, heart failure, and pneumonia.

Main Results:

  • The DID estimator with propensity score matching demonstrated significantly lower mean-squared error compared to standard DID and interrupted time-series models.
  • The DID estimator with matching also showed superior estimator coverage.
  • These findings were consistent across all evaluated health outcomes.

Conclusions:

  • Propensity score matching enhances the reliability of DID analysis when parallel trends are not met.
  • Researchers should consider DID with matching for more robust causal inference in health policy evaluations facing violated parallel trends.