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The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement
Open accessAbstract
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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“As previously shown, the overall decline was not explained by the lifestyle coaching offered to all participants but was likely due, in part, to “exhaustion” of the population most susceptible to diabetes who developed it earlier in the study.16”
From Long-term effects and effect heterogeneity of lifestyle and metformin interventions on type 2 diabetes incidence over 21 years in the US Diabetes Prevention Program randomised clinical trial (2025) · cites this paper as [16]
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Evidence based medicine: what it is and what it isn't
Sackett, D. L., Rosenberg, W. M. C., Gray, J. A. M., Haynes, R. B., & Richardson, W. S. (1996) · BMJ · doi:10.1136/bmj.312.7023.71 · Cited by 15,769
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147
Self-citationCollins, P. G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · British Journal of Cancer · doi:10.1038/bjc.2014.639 · Cited by 3,315
Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0697 · Cited by 3,267
Consensus development methods, and their use in clinical guideline development.
Murphy, M., Black, Lamping, McKee, M., Sanderson, Askham, & Marteau (1998) · Health Technology Assessment · doi:10.3310/hta2030 · Cited by 2,076
Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research
Steyerberg, E. W., Moons, K. G. M., van der Windt, D. A., Hayden, J. A., Perel, P., Schroter, S., Riley, R. D., Hemingway, H., Altman, D. G., & for the PROGRESS Group (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001381 · Cited by 1,508 · cited 1×
“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…”
MethodSelf-citationWhy Most Discovered True Associations Are Inflated
Ioannidis, J. P. A. (2008) · Epidemiology · doi:10.1097/ede.0b013e31818131e7 · Cited by 1,495
Self-citationMetalearners for estimating heterogeneous treatment effects using machine learning
Künzel, S., Sekhon, J., Bickel, P., & Yu, B. (2017) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1804597116 · Cited by 1,458
Statistics in Medicine — Reporting of Subgroup Analyses in Clinical Trials
Wang, R., Lagakos, S. W., Ware, J. H., Hunter, D. J., & Drazen, J. M. (2007) · New England Journal of Medicine · doi:10.1056/nejmsr077003 · Cited by 1,445
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Fusar‐Poli, P., Correll, C. U., Arango, C., Berk, M., Patel, V., & Ioannidis, J. P. A. (2021) · World Psychiatry · doi:10.1002/wps.20869 · Cited by 497
Self-citationZabor, E. C., Kaizer, A. M., & Hobbs, B. P. (2020) · Chest · doi:10.1016/j.chest.2020.03.013 · Cited by 294
Kent, D. M., Saver, J. L., Kasner, S. E., Nelson, J., Carroll, J. D., Châtellier, G., Dérumeaux, G., Furlan, A. J., Herrmann, H. C., Jüni, P., et al. (2021) · JAMA · doi:10.1001/jama.2021.20956 · Cited by 256
Self-citationTakahashi, K., Serruys, P. W. J. C., Fuster, V., Farkouh, M. E., Spertus, J. A., Cohen, D. J., Park, S., Park, D., Ahn, J., Kappetein, A. P., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)32114-0 · Cited by 218
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Metadata
- 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
- 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
- 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
- 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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