Patient Variability Seldom Assessed in Cost-effectiveness Studies.

Med Decis Making 2018 05 19;38(4):487-494. Epub 2018 Jan 19.

Center for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.

Background: Cost-effectiveness analysis (CEA) estimates can vary substantially across patient subgroups when patient characteristics influence preferences, outcome risks, treatment effectiveness, life expectancy, or associated costs. However, no systematic review has reported the frequency of subgroup analysis in CEA, what type of heterogeneity they address, and how often heterogeneity influences whether cost-effectiveness ratios exceed or fall below conventional thresholds.

Methods: We reviewed the CEA literature cataloged in the Tufts Medical Center CEA Registry, a repository describing cost-utility analyses published through 2016. After randomly selecting 200 of 642 articles published in 2014, we ascertained whether each study reported subgroup results and collected data on the defining characteristics of these subgroups. We identified whether any of the CEA subgroup results crossed conventional cost-effectiveness benchmarks (e.g., $100,000 per QALY) and compared characteristics of studies with and without subgroup-specific findings.

Results: Thirty-eight studies (19%) reported patient subgroup results. Articles reporting subgroup analyses were more likely to be US-based, government funded (v. drug industry- or nonprofit foundation-funded) studies, with a focus on primary or secondary (v. tertiary) prevention (P < 0.05 for comparisons). One or more patient characteristics were used to stratify CEA results 68 times within the 38 studies, with most stratifications using one characteristic (n = 47), most commonly age (n = 35). Among the 23 stratifications reported alongside average ratios in US studies, 13 produced subgroup ratios that crossed a conventional CEA ratio benchmark.

Conclusions: Most CEAs do not report any subgroup results, and those that do most often stratify only by patient age. Over half of the subgroup analyses reported could lead to different value-based decision making for at least some patients.

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http://dx.doi.org/10.1177/0272989X17746989DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6882686PMC
May 2018
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