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Kent DM, van Klaveren D, Paulus JK, et al. The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration. Ann Intern Med. 2019;172(1):W1-W25doi: 10.7326/M18-3668.
Kent, D. M., van Klaveren, D., Paulus, J. K., 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. (2020). The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration. Annals of internal medicine, 172(1), W1-W25. https://doi.org/10.7326/M18-3668
Kent, David M, et al. "The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration." Annals of internal medicine vol. 172,1 (2020): W1-W25. doi: https://doi.org/10.7326/M18-3668
Kent DM, van Klaveren D, Paulus JK, D'Agostino R, Goodman S, Hayward R, Ioannidis JPA, Patrick-Lake B, Morton S, Pencina M, Raman G, Ross JS, Selker HP, Varadhan R, Vickers A, Wong JB, Steyerberg EW. The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration. Ann Intern Med. 2020 Jan 07;172(1):W1-W25. doi: 10.7326/M18-3668. Epub 2019 Nov 12. PMID: 31711094; PMCID: PMC7750907.
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Ann Intern Med. 2020 Jan 07;172(1):W1-W25. doi: 10.7326/M18-3668. Epub 2019 Nov 12.

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

Annals of internal medicine

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

PMID: 31711094 PMCID: PMC7750907 DOI: 10.7326/M18-3668

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.

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