Impact of the PATH Statement on Analysis and Reporting of Heterogeneity of Treatment Effect in Clinical Trials: A Scoping Review

BackgroundThe PATH Statement (2020) proposed predictive modeling for examining heterogeneity in treatment effects (HTE) in randomized clinical trials (RCTs). It distinguished risk modeling, which develops a multivariable model predicting individual baseline risk of study outcomes and examines treatment effects across risk strata, from effect modeling, which directly estimates individual treatment effects from models that include treatment, multiple patient characteristics and interactions of treatment with selected characteristics. PurposeTo identify, describe and evaluate findings from reports that cite the Statement and present predictive modeling of HTE in RCTs. Data ExtractionWe identified reports using PubMed, Google Scholar, Web of Science, SCOPUS through July 5, 2024. Using double review with adjudication, we assessed consistency with Statement recommendations, credibility of HTE findings (applying criteria adapted from the Instrument to assess Credibility of Effect Modification Analyses (ICEMAN)), and clinical importance of credible findings. ResultsWe identified 65 reports (presenting 31 risk models, 41 effect models). Contrary to Statement recommendations, only 25 of 48 studies with positive overall findings included a risk model; most effect models included multiple predictors with little prior evidence for HTE. Claims of HTE were noted in 23 risk modeling and 31 effect modeling reports, but risk modeling met credibility criteria more frequently (87 vs 32 percent). For effect models, external validation of HTE findings was critical in establishing credibility. Credible HTE from either approach was usually judged clinically important (24 of 30). In 19 reports from trials suggesting overall treatment benefits, modeling identified subgroups of 5-67% of patients predicted to experience no benefit or net treatment harm. In five that found no overall benefit, subgroups of 25-60% of patients were nevertheless predicted to benefit. ConclusionsMultivariable predictive modeling identified credible, clinically important HTE in one third of 65 reports. Risk modeling found credible HTE more frequently; effect modeling analyses were usually exploratory, but external validation served to increase credibility.