Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study
Abstract 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 treatment effect. However, individual signific...
Saved in:
| Main Authors: | Eleanor Van Vogt, Anthony C. Gordon, Karla Diaz-Ordaz, Suzie Cro |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-02-01
|
| Series: | BMC Medical Research Methodology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12874-025-02489-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Estimating Treatment Effect Heterogeneity in Psychiatry: A Review and Tutorial With Causal Forests
by: Erik Sverdrup, et al.
Published: (2025-06-01) -
Association Between Periodontitis and Preeclampsia: A Bidirectional Mendelian Randomisation Analysis
by: Jiao Cao, et al.
Published: (2024-12-01) -
CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification—leveraging deep learning models for enhanced diagnostic accuracy
by: Zahra Taghados, et al.
Published: (2025-04-01) -
Causal inference and machine learning in endocrine epidemiology
by: Kosuke Inoue
Published: (2024-10-01) -
Diabetes Prediction Through Linkage of Causal Discovery and Inference Model with Machine Learning Models
by: Mi Jin Noh, et al.
Published: (2025-01-01)