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Cited by 31Health Systems, Economic Evaluations, Quality of LifeEconomic and Environmental ValuationDelphi Technique in Research

Some Health States Are Better Than Others: Using Health State Rank Order to Improve Probabilistic Analyses

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Goldhaber-Fiebert, Jeremy D., Jalal, Hawre J.·2016·Medical Decision Making
DOI 10.1177/0272989x15605091PMC PMC4794424
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Abstract

Background. Probabilistic sensitivity analyses (PSA) may lead policy makers to take nonoptimal actions due to misestimates of decision uncertainty caused by ignoring correlations. We developed a method to establish joint uncertainty distributions of quality-of-life (QoL) weights exploiting ordinal preferences over health states. Methods. Our method takes as inputs independent, univariate marginal distributions for each QoL weight and a preference ordering. It establishes a correlation matrix between QoL weights intended to preserve the ordering. It samples QoL weight values from their distributions, ordering them with the correlation matrix. It calculates the proportion of samples violating the ordering, iteratively adjusting the correlation matrix until this proportion is below an arbitrarily small threshold. We compare our method with the uncorrelated method and other methods for preserving rank ordering in terms of violation proportions and fidelity to the specified marginal distributions along with PSA and expected value of partial perfect information (EVPPI) estimates, using 2 models: 1) a decision tree with 2 decision alternatives and 2) a chronic hepatitis C virus (HCV) Markov model with 3 alternatives. Results. All methods make tradeoffs between violating preference orderings and altering marginal distributions. For both models, our method simultaneously performed best, with largest performance advantages when distributions reflected wider uncertainty. For PSA, larger changes to the marginal distributions induced by existing methods resulted in differing conclusions about which strategy was most likely optimal. For EVPPI, both preference order violations and altered marginal distributions caused existing methods to misestimate the maximum value of seeking additional information, sometimes concluding that there was no value. Conclusions. Analysts can characterize the joint uncertainty in QoL weights to improve PSA and value-of-information estimates using Open Source implementations of our method.

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Authors
Goldhaber-Fiebert, J. D., & Jalal, H. J.
Affiliations
  • Stanford Health Policy, Centers for Health Policy and Primary Care and Outcomes Research, Department of Medicine, Stanford University, Stanford, CA, USA (JDGF, HJJ)
  • University of Pittsburgh (US)
ORCID
Jalal, Hawre J.
Venue
Medical Decision Making
Publisher
SAGE Publications
Published
2016-07-10
Volume
36
Issue
8
Pages
927-940
ISSN
  • 0272-989X (ISSN-L)
  • 1552-681X
DOI
10.1177/0272989x15605091
PMC
PMC4794424
OpenAlex
W2340549666
Cited by
31
References
0
Citations by year
2026: 2 · 2025: 8 · 2024: 3 · 2023: 2 · 2022: 1 · 2021: 1 · 2020: 4 · 2019: 4 · 2018: 3 · 2017: 3
FWCI
4.82
Type
journal article
Language
en
Open access
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
  • Medical Decision Making (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
  • National Institutes of Health (5k01ag037593-04)
  • National Institute on Aging (K01-AG037593)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic and Environmental Valuation
  • Social Sciences › Sociology and Political Science › Delphi Technique in Research
Keywords
correlation matrix estimation, marginal distributions, probabilistic sensitivity analysis, value of information
MeSH terms
Clinical Decision-Making, Algorithms, Cost-Benefit Analysis, Decision Trees, Humans, Markov Chains, Probability, Quality of Life, Models, Statistical, Hepatitis C, Chronic, Uncertainty

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