When assessing causal effects, determining the target population to which the results are
intended to generalize is a critical decision. Randomized and observational studies each …

Fifty years have passed since the publication of the first regression tree algorithm. New
techniques have added capabilities that far surpass those of the early methods. Modern …

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 …

When evaluating the efficacy of social programs and medical treatments using randomized
experiments, the estimated overall average causal effect alone is often of limited value and …

It is well known that both the direction and magnitude of the treatment effect in clinical trials
are often affected by baseline patient characteristics (generally referred to as biomarkers) …

Background Tralokinumab is an anti-interleukin-13 human monoclonal antibody developed
for the treatment of severe, uncontrolled asthma. These clinical trials aimed to assess the …

Identification of subgroups of patients for whom treatment A is more effective than treatment
B, and vice versa, is of key importance to the development of personalized medicine. Tree …

The identification of patient subgroups with differential treatment effects is the first step
towards individualised treatments. A current draft guideline by the EMA discusses potentials …

Estimation of individual treatment effect in observational data is complicated due to the
challenges of confounding and selection bias. A useful inferential framework to address this …

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 …