For the analysis of time-to-event data, frequently used methods such as the log-rank test or
the Cox proportional hazards model are based on the proportional hazards assumption …

Regression models for supervised learning problems with a continuous response are
commonly understood as models for the conditional mean of the response given predictors …

The mlt package implements maximum likelihood estimation in the class of conditional
transformation models. Based on a suitable explicit parameterization of the unconditional or …

Background: Multiple sclerosis (MS) is a chronic neuroinflammatory disease affecting about
2.8 million people worldwide. Disease course after the most common diagnoses of relapsing …

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 …

We study and compare several variants of random forests tailored to prognostic models for
ordinal outcomes. Models of the conditional odds function are employed to understand the …

Discovering causal relationships from observational data is a fundamental yet challenging
task. Invariant causal prediction (ICP, Peters, Bühlmann, and Meinshausen) is a method for …

Background Accurate and interpretable models are essential for clinical decision-making,
where predictions can directly impact patient care. Machine learning (ML) survival methods …

One of the main endeavours in present‐day medicine, especially in oncological research, is
to provide evidence for individual treatment decisions (“stratified medicine”). In the pursuit of …

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