Evidence map›Paper›PMID 31711134›Full record

ArticleAnnals of internal medicine2020

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement.

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

Abstract read
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 214 papers, 14 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
214citing papers in PubMed, 14 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

214 citing papers in PubMed, 14 syntheses or guidelines pooled it.

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  7. Addressing Systematic Missing Data in the Context of Causally Interpretable Meta-analysis.Prevention science : the official journal of the Society for Prevention Research · 2023
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154 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

David M KentPredictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts (D.M.K., J.K.P., J.B.W.).
Jessica K PaulusPredictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts (D.M.K., J.K.P., J.B.W.).
David van KlaverenErasmus Medical Center, Rotterdam, the Netherlands, and Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts (D.V.).
Ralph D'AgostinoBoston University, Boston, Massachusetts (R.D.).
Steve GoodmanMeta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California (S.G., J.P.I.).
Rodney HaywardUniversity of Michigan, Ann Arbor, Michigan (R.H.).
John P A IoannidisMeta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California (S.G., J.P.I.).
Bray Patrick-LakeDuke Clinical Research Institute, Duke University, Durham, North Carolina (B.P., M.P.).
Sally MortonVirginia Polytechnic Institute and State University, Blacksburg, Virginia (S.M.).
Michael PencinaDuke Clinical Research Institute, Duke University, Durham, North Carolina (B.P., M.P.).
Gowri RamanCenter for Clinical Evidence Synthesis, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts (G.R.).
Joseph S RossSchools of Medicine and Public Health, Yale University, New Haven, Connecticut (J.S.R.).
Harry P SelkerCenter for Cardiovascular Health Services Research, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, and Tufts Clinical and Translational Science Institute, Boston, Massachusetts (H.P.S.).
Ravi VaradhanCenter on Aging and Health, Johns Hopkins University, Baltimore, Maryland (R.V.).
Andrew VickersMemorial Sloan Kettering Cancer Center, New York, New York (A.V.).
John B WongPredictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts (D.M.K., J.K.P., J.B.W.).
Ewout W SteyerbergLeiden University Medical Center, Leiden, the Netherlands (E.W.S.).

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748Patient-Centered Outcomes Research Institute 1900Patient-Centered Outcomes Research Institute RR-1705-0001
6 · The paper itself

Abstract

Heterogeneity of treatment effect (HTE) refers to the nonrandom variation in the magnitude or direction of a treatment effect across levels of a covariate, as measured on a selected scale, against a clinical outcome. In randomized controlled trials (RCTs), HTE is typically examined through a subgroup analysis that contrasts effects in groups of patients defined "1 variable at a time" (for example, male vs. female or old vs. young). The authors of this statement present guidance on an alternative approach to HTE analysis, "predictive HTE analysis." The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risks with versus without the intervention, taking into account all relevant patient attributes simultaneously. The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed using a multidisciplinary technical expert panel, targeted literature reviews, simulations to characterize potential problems with predictive approaches, and a deliberative process engaging the expert panel. The authors distinguish 2 categories of predictive HTE approaches: a "risk-modeling" approach, wherein a multivariable model predicts the risk for an outcome and is applied to disaggregate patients within RCTs to define risk-based variation in benefit, and an "effect-modeling" approach, wherein a model is developed on RCT data by incorporating a term for treatment assignment and interactions between treatment and baseline covariates. Both approaches can be used to predict differential absolute treatment effects, the most relevant scale for clinical decision making. The authors developed 4 sets of guidance: criteria to determine when risk-modeling approaches are likely to identify clinically important HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. The PATH Statement, together with its explanation and elaboration document, may guide future analyses and reporting of RCTs.

Indexed as

Treatment OutcomeClinical Decision-MakingClinical Decision RulesEvidence-Based MedicineHumansIndividualityModels, StatisticalRandomized Controlled Trials as TopicRisk Assessment

Identifiers

PMID31711134
PMCPMC7531587

What OpenQuestion holds

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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.