The use of evidence from clinical trials to support decisions for individual patients is a form of
“reference class forecasting”: implicit predictions for an individual are made on the basis of …

The identification of biomarkers plays a crucial role in personalized medicine, both in the
clinical and research settings. However, the contrast between predictive and prognostic …

BACKGROUND: Most randomized controlled trials (RCTs) and meta-analyses of RCTs
examine effect modification (also called a subgroup effect or interaction), in which the effect …

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 …

Quasi-experiments are similar to randomized controlled trials in many respects, but there are
many challenges in designing and conducting a quasi-experiment when internal validity …

Predicting future outcomes of patients is essential to clinical practice, with many prediction
models published each year. Empirical evidence suggests that published studies often have …

The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was
developed to promote the conduct of, and provide guidance for, predictive analyses of …

Precision medicine research often searches for treatment‐covariate interactions, which
refers to when a treatment effect (eg, measured as a mean difference, odds ratio, hazard …

The machine learning (ML) toolbox for estimation of heterogeneous treatment effects from
observational data is expanding rapidly, yet many of its algorithms have been evaluated …

Clinical trials have documented numerous clinical features, social characteristics, and
biomarkers that are “prescriptive” predictors of depression treatment response, that is …