A regression framework for a probabilistic measure of cost-effectiveness.

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Tytuł:: A regression framework for a probabilistic measure of cost-effectiveness.
Autorzy:: Illenberger N; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Mitra N; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Spieker AJ; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Źródło:: Health economics [Health Econ] 2022 Jul; Vol. 31 (7), pp. 1438-1451. Date of Electronic Publication: 2022 Apr 22.
Typ publikacji:: Journal Article; Research Support, N.I.H., Extramural
Język:: English
Imprint Name(s):: Original Publication: Chichester ; New York : Wiley, c1992-
MeSH Terms:: Cost-Benefit Analysis*
Female ; Humans ; Treatment Outcome
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Grant Information:: T32DK060455 United States NH NIH HHS
Contributed Indexing:: Keywords: censoring; cost-effecitveness; observational; standardization; stochastic ordering
Entry Date(s):: Date Created: 20220423 Date Completed: 20220610 Latest Revision: 20220908
Update Code:: 20240105
DOI:: 10.1002/hec.4517
PMID:: 35460149
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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.
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