Evidence map›Paper›PMID 31711094›Full record

ArticleAnnals of internal medicine2020

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration.

David M Kent, David van Klaveren, Jessica K Paulus, Ralph D'Agostino, Steve Goodman, Rodney Hayward, John P A Ioannidis, Bray Patrick-Lake, Sally Morton, Michael Pencina and 7 more

Abstract read
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In one paragraph

Article in Annals of internal medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
88citing 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.

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

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

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28 more citing papers are in PubMed but not listed here.

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

17 authors.

David M Kent
David van Klaveren
Jessica K Paulus
Ralph D'Agostino
Steve Goodman
Rodney Hayward
John P A Ioannidis
Bray Patrick-Lake
Sally Morton
Michael Pencina
Gowri Raman
Joseph S Ross
Harry P Selker
Ravi Varadhan
Andrew Vickers
John B Wong
Ewout W Steyerberg

Funding

Patient-Centered Outcomes Research Institute 1900Patient-Centered Outcomes Research Institute RR-1705-0001
6 · The paper itself

Abstract

The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed to promote the conduct of, and provide guidance for, predictive analyses of heterogeneity of treatment effects (HTE) in clinical trials. The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risk with versus without the intervention, taking into account all relevant patient attributes simultaneously, to support more personalized clinical decision making than can be made on the basis of only an overall average treatment effect. The authors distinguished 2 categories of predictive HTE approaches (a "risk-modeling" and an "effect-modeling" approach) and developed 4 sets of guidance statements: criteria to determine when risk-modeling approaches are likely to identify clinically meaningful HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. They discuss limitations of these methods and enumerate research priorities for advancing methods designed to generate more personalized evidence. This explanation and elaboration document describes the intent and rationale of each recommendation and discusses related analytic considerations, caveats, and reservations.

Indexed as

Clinical Decision-MakingTreatment OutcomeClinical Decision RulesEvidence-Based MedicineHumansIndividualityModels, StatisticalRandomized Controlled Trials as TopicRisk Assessment

Identifiers

PMID31711094
PMCPMC7750907

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.