Background In many studies, it is of interest to identify population subgroups that are
relatively homogeneous with respect to an outcome. The nature of these subgroups can …

A critique is presented of the use of tree-based partitioning algorithms to formulate
classification rules and identify subgroups from clinical and epidemiological data. It is …

Decision tree methodology is a commonly used data mining method for establishing
classification systems based on multiple covariates or for developing prediction algorithms …

In this paper we propose methods for estimating heterogeneity in causal effects in
experimental and observational studies and for conducting hypothesis tests about the …

Individuals do not respond uniformly to treatments, such as events or interventions.
Sociologists routinely partition samples into subgroups to explore how the effects of …

By comparing the performance of various tree algorithms, we can determine which one is
most useful for analyzing biomedical data. In artificial intelligence, decision trees are a …

In the fight against hard‐to‐treat diseases such as cancer, it is often difficult to discover new
treatments that benefit all subjects. For regulatory agency approval, it is more practical to …

We provide a Bayesian decision theoretic approach to finding subgroups that have elevated
treatment effects. Our approach separates the modeling of the response variable from the …

Background: Audience segmentation strategies are of increasing interest to public health
professionals who wish to identify easily defined, mutually exclusive population subgroups …

In health and social sciences, it is critically important to identify subgroups of the study
population where there is notable heterogeneity of treatment effects (HTE) with respect to …