@article{10885,
  keywords = {Humans, *Diabetes Mellitus, Type 2/drug therapy, Glycated Hemoglobin, Cohort Studies, Precision medicine, Dipeptidyl Peptidase 4/therapeutic use, Sodium-Glucose Transporter 2/therapeutic use, Hypoglycemic Agents/therapeutic use, *Dipeptidyl-Peptidase IV Inhibitors/therapeutic use, *Sodium-Glucose Transporter 2 Inhibitors/therapeutic use, Treatment Outcome, Causal forest, Counterfactual prediction, Generalized random forests, Heterogeneous treatment effects, machine learning, Treatment effect heterogeneity, Treatment selection, Type 2 diabetes},
  author = {A. Venkatasubramaniam and B. Mateen and B. Shields and A. Hattersley and A. Jones and S. Vollmer and J. Dennis},
  title = {Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine},
  year = {2023},
  journal = {BMC Med Inform Decis Mak},
  volume = {23},
  edition = {2023/06/17},
  number = {1},
  pages = {110},
  isbn = {1472-6947},
  doi = {10.1186/s12911-023-02207-2},
  note = {1472-6947
Venkatasubramaniam, Ashwini
Mateen, Bilal A
Shields, Beverley M
Hattersley, Andrew T
Jones, Angus G
Vollmer, Sebastian J
Dennis, John M
MR/W003988/1/MRC_/Medical Research Council/United Kingdom
Journal Article
England
BMC Med Inform Decis Mak. 2023 Jun 16;23(1):110. doi: 10.1186/s12911-023-02207-2.},
  language = {eng},
}