Predictive Modeling of Heterogeneous Treatment Effects in RCTs
Joe V. Selby; Carolien C. H. M. Maas; Bruce Fireman; David M. Kent · 2025 · JAMA Network Open
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Importance: The Predictive Approaches to Treatment Effect Heterogeneity (PATH) Statement of 2020 proposed predictive modeling for identifying heterogeneity in treatment effects (HTE) in randomized clinical trials (RCTs). It described 2 approaches: risk modeling, which develops a multivariable model predicting individual baseline risk of study outcomes and then examines treatment effects across strata of predicted risk, and effect modeling, which develops a model that directly predicts individual treatment effects using a variety of regression and machine learning methods. Objective: To identif
Abstract by Joe V. Selby; Carolien C. H. M. Maas; Bruce Fireman; David M. Kent, JAMA Network Open (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1001/jamanetworkopen.2025.22390