TY - JOUR
T1 - The Predictive Approaches to Treatment effect Heterogeneity (PATH) statement
AU - Kent, David M.
AU - Paulus, Jessica K.
AU - Van Klaveren, David
AU - D'Agostino, Ralph
AU - Goodman, Steve
AU - Hayward, Rodney
AU - Ioannidis, John P.A.
AU - Patrick-Lake, Bray
AU - Morton, Sally
AU - Pencina, Michael
AU - Raman, Gowri
AU - Ross, Joseph S.
AU - Selker, Harry P.
AU - Varadhan, Ravi
AU - Vickers, Andrew
AU - Wong, John B.
AU - Steyerberg, Ewout W.
N1 - Funding Information:
Financial Support: Development of the PATH Statement was supported through contract SA.Tufts.PARC.OSCO.2018.01.25 from the PCORI Predictive Analytics Resource Center. This work was also informed by a 2018 conference (“Evidence and the Individual Patient: Understanding Heterogeneous Treatment Effects for Patient-Centered Care”) convened by the National Academy of Medicine and funded through a PCORI Eugene Washington Engagement Award (1900-TMC).
Funding Information:
Disclosures: Dr. Kent reports grants from PCORI during the conduct of the study. Dr. Hayward reports grants from the National Institute of Diabetes and Digestive and Kidney Diseases and the Veterans Affairs Health Services Research and Development Service during the conduct of the study. Dr. Pencina reports grants from PCORI (Tufts Subaward) during the conduct of the study; grants from Sanofi/Regeneron, Am-gen, and Bristol-Myers Squibb outside the submitted work; and personal fees from Boehringer Ingelheim and Merck outside the submitted work. Dr. Ross reports personal fees from PCORI during the conduct of the study and grants from the U.S. Food and Drug Administration, Medtronic, Johnson & Johnson, the Centers for Medicare & Medicaid Services, Blue Cross Blue Shield Association, the Agency for Healthcare Research and Quality, the National Institutes of Health (National Heart, Lung, and Blood Institute), and Laura and John Arnold Foundation outside the submitted work. Dr. Varadhan reports personal fees from Tufts University during the conduct of the study. Dr. Vickers reports grants from the National Institutes of Health during the conduct of the study. Dr. Wong reports grants from PCORI during the conduct of the study. Dr. Stey-erberg reports royalties from Springer for his book Clinical Prediction Models. Authors not named here have disclosed no conflicts of interest. Disclosures can also be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms .do?msNum=M18-3667.
Publisher Copyright:
© 2019 American College of Physicians.
PY - 2020/1/7
Y1 - 2020/1/7
N2 - 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 "riskmodeling" 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.
AB - 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 "riskmodeling" 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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U2 - 10.7326/M18-3667
DO - 10.7326/M18-3667
M3 - Article
C2 - 31711134
AN - SCOPUS:85077796834
SN - 0003-4819
VL - 172
SP - 35
EP - 45
JO - Annals of internal medicine
JF - Annals of internal medicine
IS - 1
ER -