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How robust are reference pricing studies on outpatient medical procedures? Three different preprocessing techniques

Timothy Tyler Brown1, Juan Pablo Atal1

  • 1School of Public Health, University of California, Berkeley, California, USA.

Health Economics
|November 20, 2018
PubMed
Summary

Evaluating healthcare policies like reference pricing requires careful data preprocessing. This study found that while different methods offer valuable robustness checks, they generally confirm previous findings on patient care and costs.

Keywords:
difference-in-differencesexact matchinggenetic matchingoutpatient procedurespropensity scoresreference pricing

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Area of Science:

  • Health economics
  • Policy evaluation
  • Econometrics

Background:

  • Evaluating non-randomized policies, especially with nonlinear data, often relies on assumptions lacking theoretical support.
  • The introduction of reference pricing for outpatient procedures is a key policy example requiring robust impact assessment.

Purpose of the Study:

  • To assess the robustness of previously published difference-in-differences results concerning reference pricing.
  • To examine the impact of different data preprocessing techniques on policy evaluation outcomes.

Main Methods:

  • Revisiting published difference-in-differences results using propensity score reweighting, exact matching, and genetic matching.
  • Comparing preprocessing methods based on their ability to improve covariate balance and reduce model dependence.
  • Assessing the balancing of higher-order moments, crucial for nonlinear data generating processes.

Main Results:

  • Preprocessing techniques served as valuable robustness checks, revealing some sensitivity of results to the chosen method.
  • Propensity score reweighting balances covariates but may not adequately address higher-order moments in nonlinear settings.
  • Exact matching and genetic matching, designed to balance higher-order moments, generally produced results consistent with published findings.

Conclusions:

  • While preprocessing methods show some variation, they generally confirm the robustness of the original reference pricing impact estimates.
  • The study highlights the importance of advanced matching techniques for reliable policy evaluation in complex data environments.
  • Findings suggest that established difference-in-differences results for reference pricing are largely stable across different robust preprocessing strategies.