Evidence map›Paper›PMID 26377369›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2016

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

Jeremy D Goldhaber-Fiebert, Hawre J Jalal

Abstract read
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
  4. Cost and Cost-Effectiveness of Treating Human Epidermal Growth Factor Receptor 2-Low Metastatic Breast Cancer.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2025
    Article
  5. Article
  6. Article
  7. Microsimulation Estimates of Decision Uncertainty and Value of Information Are Biased but Consistent.Medical decision making : an international journal of the Society for Medical Decision Making · 2025
    Article
  8. Article
  9. An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example.Medical decision making : an international journal of the Society for Medical Decision Making · 2023
    Article
  10. Simulating Study Data to Support Expected Value of Sample Information Calculations: A Tutorial.Medical decision making : an international journal of the Society for Medical Decision Making · 2022
    Article
  11. Prioritizing Additional Data Collection to Reduce Decision Uncertainty in the HIV/AIDS Response in 6 US Cities: A Value of Information Analysis.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2020
    Article
  12. Cost-effectiveness of Active Identification and Subsequent Colonoscopy Surveillance of Lynch Syndrome Cases.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2020
    Article
  13. Article
  14. Calculating the Expected Value of Sample Information in Practice: Considerations from 3 Case Studies.Medical decision making : an international journal of the Society for Medical Decision Making · 2020
    Article
  15. Article
  16. Article
  17. Observational
  18. Article
  19. A Gaussian Approximation Approach for Value of Information Analysis.Medical decision making : an international journal of the Society for Medical Decision Making · 2018
    Article
  20. An Overview of R in Health Decision Sciences.Medical decision making : an international journal of the Society for Medical Decision Making · 2017
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jeremy D Goldhaber-FiebertStanford Health Policy, Centers for Health Policy and Primary Care and Outcomes Research, Department of Medicine, Stanford University, Stanford, CA, USA (JDGF, HJJ)
Hawre J JalalStanford Health Policy, Centers for Health Policy and Primary Care and Outcomes Research, Department of Medicine, Stanford University, Stanford, CA, USA (JDGF, HJJ)

Funding

Cost-effective chronic disease prevention in developing countriesK01AG037593 · NIA · STANFORD UNIVERSITY · PI GOLDHABER-FIEBERT, JEREMY DAVID · 2011 to 2015
$637k
NIA NIH HHS K01 AG037593
6 · The paper itself

Abstract

backgroundProbabilistic 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.

methodsOur 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.

resultsAll 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.

conclusionsAnalysts can characterize the joint uncertainty in QoL weights to improve PSA and value-of-information estimates using Open Source implementations of our method.

Indexed as

Decision TreesModels, StatisticalProbabilityQuality of LifeAlgorithmsClinical Decision-MakingCost-Benefit AnalysisHepatitis C, ChronicHumansMarkov ChainsUncertaintybiascorrelated parametersexpected value of partial perfect informationjoint distributionparameter correlationprobabilistic sensitivity analysisvalue of information

Identifiers

PMID26377369
PMCPMC4794424

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.