Evidence map›Paper›PMID 39367175›Full record

ArticlePharmacoEconomics2025

How Much Better is Faster? Empirical Tests of QALY Assumptions in Health-Outcome Sequences.

F Reed Johnson, John J Sheehan, Semra Ozdemir, Matthew Wallace, Jui-Chen Yang

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Article in PharmacoEconomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

F Reed JohnsonDepartment of Population Health Sciences, Duke University School of Medicine, 215 Morris St, Durham, NC, 27701, USA.
John J SheehanValue and Evidence, Neuroscience, Janssen Scientific Affairs, Titusville, NJ, USA.
Semra OzdemirDepartment of Population Health Sciences, Duke University School of Medicine, 215 Morris St, Durham, NC, 27701, USA. semra.ozdemir@duke.edu.ORCID http://orcid.org/0000-0001-7556-1601
Matthew WallaceDuke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.
Jui-Chen YangDuke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study was designed to test hypotheses regarding the path dependence of health-outcome values in the form of linear additivity of health-state utilities and diminishing marginal utility of health outcomes.

methodsWe employed a discrete-choice experiment to quantify patient treatment preferences for major depressive disorder. In a series of choice questions, participants evaluated seven symptom-improvement sequences and out-of-pocket costs over 6-week durations. Money-equivalent values were derived from a deductive latent-class mixed-logit analysis.

resultsThe discrete-choice experiment was completed by 751 respondents with self-reported major depressive disorder recruited from an online commercial panel. The class-membership probability was 0.83 for latent-class preferences consistent with supporting relative importance weights for all symptom-improvement sequences in the study design. First, we found strong support for diminishing marginal utility in symptom-improvement sequences. The money-equivalent value of an initial week of normal mood was $147 (95% confidence interval: $128, $166) and a second week of normal mood was $70 ($49, $91). Furthermore, for short treatment durations where conventional discounting was not a factor, equivalent changes in health status were valued more highly for an earlier onset of effect: holding subsequent symptom patterns constant, $338 (211, 454) versus $70 (49, 91) for improvements starting in week 2 versus week 3 and $147 ($128, $166) versus $29 (-$4, $64) for improvements starting in week 3 versus week 4.

conclusionsOur findings imply that conventional quality-adjusted life-year calculations in which health values are assumed to be path independent can understate the value of health improvements that appear earlier in a sequence.

Indexed as

Major Depressive DisorderPatient PreferenceQuality-Adjusted Life YearsAdultChoice BehaviorCost-Benefit AnalysisFemaleHealth ExpendituresHumansMaleMiddle AgedSurveys and QuestionnairesTime Factors

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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.