Cited by 474Statistical Methods in Clinical TrialsAdvanced Causal Inference TechniquesMeta-analysis and systematic reviews

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The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement

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Kent, David M., Paulus, Jessica K., van Klaveren, David, D'Agostino, Ralph, Goodman, Steve, Hayward, Rodney, Ioannidis, John P.A., Patrick-Lake, Bray, Morton, Sally, Pencina, Michael, Raman, Gowri, Ross, Joseph S., Selker, Harry P., Varadhan, Ravi, Vickers, Andrew, Wong, John B., Steyerberg, Ewout W.·2020·Annals of Internal Medicine

DOI 10.7326/m18-3667PMC PMC7531587

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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.

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“Although guidance is expanding regarding optimal approaches to prediction modeling (such as PROGRESS [Prognosis Research Strategy] [27, 28] and TRIPOD [Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis] [29]) and some guidance exists for HTE analysis (such as the Patient-C…”
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Authors: Kent, D. M., Paulus, J. K., van Klaveren, D., D'Agostino, R., Goodman, S., Hayward, R., Ioannidis, J. P. A., Patrick-Lake, B., Morton, S., Pencina, M., Raman, G., Ross, J. S., Selker, H. P., Varadhan, R., Vickers, A., Wong, J. B., & Steyerberg, E. W.
Affiliations: Tufts Medical Center (US)
Erasmus 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.)
Boston University, Boston, Massachusetts (R.D.)
Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California (S.G., J.P.I.)
University of Michigan (US)
Stanford University (US)
Duke Clinical Research Institute, Duke University, Durham, North Carolina (B.P., M.P.)
Virginia Tech (US)
Duke University (US)
Yale University (US)
Johns Hopkins University (US)
Memorial Sloan Kettering Cancer Center (US)
Leiden University Medical Center (NL)
ORCID: Kent, David M.
Paulus, Jessica K.
Hayward, Rodney
Ioannidis, John P.A.
Pencina, Michael
Raman, Gowri
Ross, Joseph S.
Selker, Harry P.
Varadhan, Ravi
Vickers, Andrew
Wong, John B.
Steyerberg, Ewout W.
Venue: Annals of Internal Medicine
Publisher: American College of Physicians
Published: 2020-01-07
Volume: 172
Issue: 1
Pages: 35-45
ISSN: 0003-4819 (ISSN-L)
1539-3704
DOI: 10.7326/m18-3667
PMC: PMC7531587
OpenAlex: W2984947744
Cited by: 469
References: 83
Citations by year: 2026: 101 · 2025: 75 · 2024: 76 · 2023: 80 · 2022: 46 · 2021: 65 · 2020: 21
FWCI: 26.47
Type: journal article
Language: en
Open access: green
scholarlypublications.universiteitleiden.nl
Licence: www.acpjournals.org/journal/aim/text-and-data-mining (tdm)
Locations: Annals of Internal Medicine (publishedVersion)
PubMed (publishedVersion)
EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
Leiden Repository (Leiden University) (submittedVersion)
EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Funders: Patient-Centered Outcomes Research Institute (RR-1705-0001)
National Cancer Institute (P30CA008748)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Keywords: treatment effect heterogeneity, risk modeling, subgroup analysis, treatment effect modification, reporting guidelines, randomized controlled trials, clinical decision-making
MeSH terms: Clinical Decision-Making, Clinical Decision Rules, Humans, Individuality, Models, Statistical, Randomized Controlled Trials as Topic, Treatment Outcome, Risk Assessment, Evidence-Based Medicine

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