AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Treatment Effect Heterogeneity

Showing 1 to 7 of 7 articles

Clear Filters

Heterogeneous treatment effects of coronary artery bypass grafting in ischemic cardiomyopathy: A machine learning causal forest analysis.

The Journal of thoracic and cardiovascular surgery
OBJECTIVES: We aim to evaluate the heterogeneous treatment effects of coronary artery bypass grafting in patients with ischemic cardiomyopathy and to identify a group of patients to have greater benefits from coronary artery bypass grafting compared ...

A Tutorial Introduction to Heterogeneous Treatment Effect Estimation with Meta-learners.

Administration and policy in mental health
Psychotherapy has been proven to be effective on average, though patients respond very differently to treatment. Understanding which characteristics are associated with treatment effect heterogeneity can help to customize therapy to the individual pa...

A new method for clustered survival data: Estimation of treatment effect heterogeneity and variable selection.

Biometrical journal. Biometrische Zeitschrift
We recently developed a new method random-intercept accelerated failure time model with Bayesian additive regression trees (riAFT-BART) to draw causal inferences about population treatment effect on patient survival from clustered and censored surviv...

Machine learning approaches to evaluate heterogeneous treatment effects in randomized controlled trials: a scoping review.

Journal of clinical epidemiology
BACKGROUND AND OBJECTIVES: Estimating heterogeneous treatment effects (HTEs) in randomized controlled trials (RCTs) has received substantial attention recently. This has led to the development of several statistical and machine learning (ML) algorith...

Survival causal rule ensemble method considering the main effect for estimating heterogeneous treatment effects.

Statistics in medicine
With an increasing focus on precision medicine in medical research, numerous studies have been conducted in recent years to clarify the relationship between treatment effects and patient characteristics. The treatment effects for patients with differ...

Two-step pragmatic subgroup discovery for heterogeneous treatment effects analyses: perspectives toward enhanced interpretability.

European journal of epidemiology
Effect heterogeneity analyses using causal machine learning algorithms have gained popularity in recent years. However, the interpretation of estimated individualized effects requires caution because insights from these data-driven approaches might b...

Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study.

BMC medical research methodology
BACKGROUND: Classical approaches to subgroup analysis in randomised controlled trials (RCTs) to identify heterogeneous treatment effects (HTEs) involve testing the interaction between each pre-specified possible treatment effect modifier and the trea...