Estimating and testing sequential causal effects based on alternative G-formula: an observational study of the influence of early diagnosis on survival of cardia cancer
From MaRDI portal
Publication:6562710
DOI10.1080/03610918.2022.2060511MaRDI QIDQ6562710
Publication date: 27 June 2024
Published in: Communications in Statistics. Simulation and Computation (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Unnamed Item
- Identifying and estimating net effects of treatments in sequential causal inference
- New \(G\)-formula for the sequential causal effect and blip effect of treatment in sequential causal inference
- Regret-Regression for Optimal Dynamic Treatment Regimes
- Structural Nested Mean Models for Assessing Time-Varying Effect Moderation
- The central role of the propensity score in observational studies for causal effects
- Optimal Dynamic Treatment Regimes
- A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
- Optimal Structural Nested Models for Optimal Sequential Decisions
- Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions
- Longitudinal Data Analysis
- Targeted maximum likelihood estimation for dynamic treatment regimes in sequentially randomized controlled trials
This page was built for publication: Estimating and testing sequential causal effects based on alternative G-formula: an observational study of the influence of early diagnosis on survival of cardia cancer
