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A regression framework for a probabilistic measure of cost‐effectiveness

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  • Nicholas Illenberger
  • Nandita Mitra
  • Andrew J. Spieker

Abstract

To make informed health policy decisions regarding a treatment, we must consider both its cost and its clinical effectiveness. In past work, we introduced the net benefit separation (NBS) as a novel measure of cost‐effectiveness. The NBS is a probabilistic measure that characterizes the extent to which a treated patient will be more likely to experience benefit as compared to an untreated patient. Due to variation in treatment response across patients, uncovering factors that influence cost‐effectiveness can assist policy makers in population‐level decisions regarding resource allocation. In this paper, we introduce a regression framework for NBS in order to estimate covariate‐specific NBS and find determinants of variation in NBS. Our approach is able to accommodate informative cost censoring through inverse probability weighting techniques, and addresses confounding through a semiparametric standardization procedure. Through simulations, we show that NBS regression performs well in a variety of common scenarios. We apply our proposed regression procedure to a realistic simulated data set as an illustration of how our approach could be used to investigate the association between cancer stage, comorbidities and cost‐effectiveness when comparing adjuvant radiation therapy and chemotherapy in post‐hysterectomy endometrial cancer patients.

Suggested Citation

  • Nicholas Illenberger & Nandita Mitra & Andrew J. Spieker, 2022. "A regression framework for a probabilistic measure of cost‐effectiveness," Health Economics, John Wiley & Sons, Ltd., vol. 31(7), pages 1438-1451, July.
    • Handle: RePEc:wly:hlthec:v:31:y:2022:i:7:p:1438-1451
    DOI: 10.1002/hec.4517
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    References listed on IDEAS

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    4. Andrew J. Spieker & Emily M. Ko & Jason A. Roy & Nandita Mitra, 2020. "Nested g‐computation: a causal approach to analysis of censored medical costs in the presence of time‐varying treatment," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1189-1208, November.
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