The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement

协变量 医学 随机对照试验 子群分析 比例(比率) 结果(博弈论) 计量经济学 荟萃分析 机器学习 计算机科学 内科学 物理 量子力学 经济 数学 数理经济学
作者
David M. Kent,Jessica K. Paulus,David V. Klaveren,Ralph B. D’Agostino,Steven N. Goodman,Rodney A. Hayward,John P. A. Ioannidis,Bray Patrick‐Lake,Sally C. Morton,Michael J. Pencina,Gowri Raman,Joseph S. Ross,Harry P. Selker,Ravi Varadhan,Andrew Julian Vickers,John B. Wong,Ewout Willem Steyerberg
出处
期刊:Annals of Internal Medicine [American College of Physicians]
卷期号:172 (1): 35-45 被引量:469
标识
DOI:10.7326/m18-3667
摘要

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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