Evidence map›Paper›PMID 29351053›Full record

ReviewMedical decision making : an international journal of the Society for Medical Decision Making2018

Patient Variability Seldom Assessed in Cost-effectiveness Studies.

Tara A Lavelle, David M Kent, Christine M Lundquist, Teja Thorat, Joshua T Cohen, John B Wong, Natalia Olchanski, Peter J Neumann

Abstract readReview
In one paragraph

Review in Medical decision making : an international journal of the Society for Medical Decision Making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Review
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  5. A Machine-Learning Approach for Estimating Subgroup- and Individual-Level Treatment Effects: An Illustration Using the 65 Trial.Medical decision making : an international journal of the Society for Medical Decision Making · 2022
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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

8 authors.

Tara A LavelleCenter for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.
David M KentPredictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.
Christine M LundquistPredictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.
Teja ThoratCenter for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.
Joshua T CohenCenter for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.
John B WongInstitute for Clinical Research and Health Policy Studies, Tufts Medical Center, Division of Clinical Decision Making, Boston, MA, USA.
Natalia OlchanskiCenter for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.
Peter J NeumannCenter for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.

Funding

Value of Personalized Risk InformationU01NS086294 · NINDS · TUFTS MEDICAL CENTER · PI KENT, DAVID M, NEUMANN, PETER · 2013 to 2017
$2.6M
NINDS NIH HHS U01 NS086294
6 · The paper itself

Abstract

backgroundCost-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.

methodsWe 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.

resultsThirty-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.

conclusionsMost 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.

Indexed as

Data Interpretation, StatisticalAge FactorsCost-Benefit AnalysisGlobal HealthHumansPrecision MedicineQuality-Adjusted Life YearsSex Factorscost-effectivenessheterogeneitysubgroups

Identifiers

PMID29351053
PMCPMC6882686

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Registered trials

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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.