Estimating average treatment effects for clustered RCTs with recruitment bias

DOI10.1002/sim.9957zbMATH Open1548.62427MaRDI QIDQ6630292

Peter Z. Schochet

Publication date: 31 October 2024

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

generalized estimating equationsinverse probability weightingclustered RCTspropensity score modelsrecruitment bias

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Longitudinal data analysis using generalized linear models
• Matching methods for causal inference: a review and a look forward
• Asymptotic theory for clustered samples
• A specification test for the propensity score using its distribution conditional on participation
• Principal Stratification in Causal Inference
• The central role of the propensity score in observational studies for causal effects
• Model-Based Direct Adjustment
• Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
• Causal Inference for Statistics, Social, and Biomedical Sciences
• Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
• Principal Stratification Analysis Using Principal Scores
• A Generalization of Sampling Without Replacement From a Finite Universe
• Design of observational studies
• Design-Based Ratio Estimators and Central Limit Theorems for Clustered, Blocked RCTs
• A doubly robust weighting estimator of the average treatment effect on the treated
• Propensity score methods for observational studies with clustered data: a review

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