Identification of subpopulations with distinct treatment benefit rate using the Bayesian tree
DOI10.1002/bimj.201500180zbMath1353.62127OpenAlexW2467359528WikidataQ50555961 ScholiaQ50555961MaRDI QIDQ2833472
Xiaochun Li, Ya-Dong Wang, Alfred E. Buxton, Yang Hu, Peng-Sheng Chen, Changyu Shen
Publication date: 18 November 2016
Published in: Biometrical Journal (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1002/bimj.201500180
Bayesian analysismatchingmortalityclinical trialsubgroup analysiscausal inferencesubpopulationBayesian classification treeheterogeneity of treatment effectimplantable cardioverter defibrillatorstreatment benefit rate
Lua error in Module:PublicationMSCList at line 37: attempt to index local 'msc_result' (a nil value).
Related Items (1)
Cites Work
- Estimating treatment effect heterogeneity in randomized program evaluation
- Individual treatment effects in randomized trials with binary outcomes.
- Treatment Benefit and Treatment Harm Rate to Characterize Heterogeneity in Treatment Effect
- Evaluating marker-guided treatment selection strategies
- Combining biomarkers to optimize patient treatment recommendations
- The central role of the propensity score in observational studies for causal effects
- A Bayesian CART algorithm
- Estimating Individualized Treatment Rules Using Outcome Weighted Learning
- Assessing Treatment‐Selection Markers using a Potential Outcomes Framework
- Nonparametric regression estimates using misclassified binary responses
- A Robust Method for Estimating Optimal Treatment Regimes
- A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates
- Effectively Selecting a Target Population for a Future Comparative Study
- Inference for Subgroup Analysis With a Structured Logistic-Normal Mixture Model
- Large Sample Properties of Matching Estimators for Average Treatment Effects
This page was built for publication: Identification of subpopulations with distinct treatment benefit rate using the Bayesian tree
