A flexible approach for causal inference with multiple treatments and clustered survival outcomes
From MaRDI portal
Publication:6629347
DOI10.1002/SIM.9548zbMATH Open1547.62275MaRDI QIDQ6629347
Ronald D. Ennis, Joseph W. Hogan, Liangyuan Hu, Jiayi Ji
Publication date: 29 October 2024
Published in: Statistics in Medicine (Search for Journal in Brave)
Cites Work
- BART: Bayesian additive regression trees
- A flexible sensitivity analysis approach for unmeasured confounding with multiple treatments and a binary outcome with application to SEER-Medicare lung cancer data
- Assessing lack of common support in causal inference using Bayesian nonparametrics: Implications for evaluating the effect of breastfeeding on children's cognitive outcomes
- The specification of the propensity score in multilevel observational studies
- Association, causation, and marginal structural models.
- Survival analysis. Techniques for censored and truncated data.
- Covariance adjustment in radomized experiments and observational studies
- Automated versus do-it-yourself methods for causal inference: lessons learned from a data analysis competition
- Additive Mixed Effect Model for Clustered Failure Time Data
- Causal Inference on the Difference of the Restricted Mean Lifetime Between Two Groups
- A study of R2 measure under the accelerated failure time models
- A generalized additive model approach to time-to-event analysis
- Super Learner
- A comparison of parametric propensity methods for causal inference with multiple treatments and a binary outcome
- Estimating heterogeneous survival treatment effect in observational data using machine learning
Related Items (1)
This page was built for publication: A flexible approach for causal inference with multiple treatments and clustered survival outcomes
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6629347)
