Evidence map›Paper›PMID 23163976›Full record

ReviewBMC medical research methodology2012

A framework for organizing and selecting quantitative approaches for benefit-harm assessment.

Milo A Puhan, Sonal Singh, Carlos O Weiss, Ravi Varadhan, Cynthia M Boyd

Registry-linked trialAbstract readComparative StudyReview
In one paragraph

Review in BMC medical research methodology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05237635 (Defining Decision Thresholds for Judgments on Health Benefits and Harms Using the GRADE Evidence to Decision), which is not on this map. Cited by 27 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 2 pooled it
–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.

NCT05237635 naunknown statusnot on this mapstarted 2020, after this paper: background citation

Defining Decision Thresholds for Judgments on Health Benefits and Harms Using the GRADE Evidence to Decision (EtD) Frameworks: Protocol for a Randomized Methodological Study (GRADE-THRESHOLD)

TypeinterventionalSponsorMcMaster UniversityRan2020 to 2022Enrolled1,406ConditionsDecision Support Techniques, GRADE ApproachArmsdescriptive case-scenarios
3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  2. Pooled it
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  12. To Expand the Evidence Base About Harms from Tests and Treatments.Journal of general internal medicine · 2021
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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

5 authors.

Milo A PuhanJohns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Room E6153, Baltimore, MD 21205, USA. mpuhan@jhsph.edu
Sonal Singh
Carlos O Weiss
Ravi Varadhan
Cynthia M Boyd

Funding

Treatment Burden in Older Adults with Diabetes and MultimorbidityK23AG032910 · NIA · JOHNS HOPKINS UNIVERSITY · PI BOYD, CYNTHIA MELINDA · 2009 to 2013
$533k
NIA NIH HHS K23 AG032910
6 · The paper itself

Abstract

backgroundSeveral quantitative approaches for benefit-harm assessment of health care interventions exist but it is unclear how the approaches differ. Our aim was to review existing quantitative approaches for benefit-harm assessment and to develop an organizing framework that clarifies differences and aids selection of quantitative approaches for a particular benefit-harm assessment.

methodsWe performed a review of the literature to identify quantitative approaches for benefit-harm assessment. Our team, consisting of clinicians, epidemiologists, and statisticians, discussed the approaches and identified their key characteristics. We developed a framework that helps investigators select quantitative approaches for benefit-harm assessment that are appropriate for a particular decisionmaking context.

resultsOur framework for selecting quantitative approaches requires a concise definition of the treatment comparison and population of interest, identification of key benefit and harm outcomes, and determination of the need for a measure that puts all outcomes on a single scale (which we call a benefit and harm comparison metric). We identified 16 quantitative approaches for benefit-harm assessment. These approaches can be categorized into those that consider single or multiple key benefit and harm outcomes, and those that use a benefit-harm comparison metric or not. Most approaches use aggregate data and can be used in the context of single studies or systematic reviews. Although the majority of approaches provides a benefit and harm comparison metric, only four approaches provide measures of uncertainty around the benefit and harm comparison metric (such as a 95 percent confidence interval). None of the approaches considers the actual joint distribution of benefit and harm outcomes, but one approach considers competing risks when calculating profile-specific event rates. Nine approaches explicitly allow incorporating patient preferences.

conclusionThe choice of quantitative approaches depends on the specific question and goal of the benefit-harm assessment as well as on the nature and availability of data. In some situations, investigators may identify only one appropriate approach. In situations where the question and available data justify more than one approach, investigators may want to use multiple approaches and compare the consistency of results. When more evidence on relative advantages of approaches accumulates from such comparisons, it will be possible to make more specific recommendations on the choice of approaches.

Indexed as

Computer GraphicsEvidence-Based MedicineHumansQuality Indicators, Health CareReproducibility of ResultsResearch DesignRisk AssessmentSurveys and QuestionnairesTreatment Outcome

Identifiers

PMID23163976
PMCPMC3572426

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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.