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A matching method for improving covariate balance in cost‐effectiveness analyses

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  • Jasjeet Singh Sekhon
  • Richard D. Grieve

Abstract

In cost‐effectiveness analyses (CEA) that use randomized controlled trials (RCTs), covariates of prognostic importance may be imbalanced and warrant adjustment. In CEA that use non‐randomized studies (NRS), the selection on observables assumption must hold for regression and matching methods to be unbiased. Even in restricted circumstances when this assumption is plausible, a key concern is how to adjust for imbalances in observed confounders. If the propensity score is misspecified, the covariates in the matched sample will be imbalanced, which can lead to conditional bias. To address covariate imbalance in CEA based on RCTs and NRS, this paper considers Genetic Matching. This matching method uses a search algorithm to directly maximize covariate balance. We compare Genetic and propensity score matching in Monte Carlo simulations and two case studies, CEA of pulmonary artery catheterization, based on an RCT and an NRS. The simulations show that Genetic Matching reduces the conditional bias and root mean squared error compared with propensity score matching. Genetic Matching achieves better covariate balance than the unadjusted analyses of the RCT data. In the NRS, Genetic Matching improves on the balance obtained from propensity score matching and gives substantively different estimates of incremental cost‐effectiveness. We conclude that Genetic Matching can improve balance on measured covariates in CEA that use RCTs and NRS, but with NRS, this will be insufficient to reduce bias; the selection on observables assumption must also hold. Copyright © 2011 John Wiley & Sons, Ltd.

Suggested Citation

  • Jasjeet Singh Sekhon & Richard D. Grieve, 2012. "A matching method for improving covariate balance in cost‐effectiveness analyses," Health Economics, John Wiley & Sons, Ltd., vol. 21(6), pages 695-714, June.
  • Handle: RePEc:wly:hlthec:v:21:y:2012:i:6:p:695-714
    DOI: 10.1002/hec.1748
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    Cited by:

    1. Deidda, Manuela & Geue, Claudia & Kreif, Noemi & Dundas, Ruth & McIntosh, Emma, 2019. "A framework for conducting economic evaluations alongside natural experiments," Social Science & Medicine, Elsevier, vol. 220(C), pages 353-361.
    2. Vera Antonia Büchner & Vera Hinz & Jonas Schreyögg, 2016. "Health systems: changes in hospital efficiency and profitability," Health Care Management Science, Springer, vol. 19(2), pages 130-143, June.
    3. Jason J. Sauppe & Sheldon H. Jacobson & Edward C. Sewell, 2014. "Complexity and Approximation Results for the Balance Optimization Subset Selection Model for Causal Inference in Observational Studies," INFORMS Journal on Computing, INFORMS, vol. 26(3), pages 547-566, August.
    4. Timothy Tyler Brown & Juan Pablo Atal, 2019. "How robust are reference pricing studies on outpatient medical procedures? Three different preprocessing techniques applied to difference‐in differences," Health Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 280-298, February.
    5. Alarcón, Silverio & Sánchez, Mercedes, 2016. "Is there a virtuous circle relationship between innovation activities and exports? A comparison of food and agricultural firms," Food Policy, Elsevier, vol. 61(C), pages 70-79.
    6. Lindlbauer, Ivonne & Schreyögg, Jonas & Winter, Vera, 2016. "Changes in technical efficiency after quality management certification: A DEA approach using difference-in-difference estimation with genetic matching in the hospital industry," European Journal of Operational Research, Elsevier, vol. 250(3), pages 1026-1036.

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