Precision medicine has revolutionised cancer treatments; however, actionable biomarkers
remain scarce. To address this, we develop the Oncology Biomarker Discovery (OncoBird) …

Purpose We aimed to develop an accurate prognostic model to identify suitable candidates
for definitive radiation therapy (DRT) in addition to palliative chemotherapy (PCT) among …

Background Recent developments in causal inference and machine learning (ML) allow for
the estimation of individualized treatment effects (ITEs), which reveal whether treatment …

The document provides details about the cut-point selection of model-based forests and
causal forests, the comparative results of adaptive and honest forests for the simulations …

The investigation of subgroups is an integral part of randomized clinical trials. Exploration of
treatment effect heterogeneity is typically performed by covariate-adjusted analyses …

The estimation of heterogeneous treatment effects has attracted considerable interest in
many disciplines, most prominently in medicine and economics. Contemporary research has …

The R package BioPred offers a suite of tools for subgroup and biomarker analysis in
precision medicine. Leveraging Extreme Gradient Boosting (XGBoost) along with propensity …

Objective Variability exists in the degree of improvement patients experience following
exercise-based interventions (EBIs) for knee osteoarthritis (KOA), but understanding of this …

Causal inference and machine learning are typically introduced in the social sciences
separately as theoretically distinct methodological traditions. However, applications of …

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