Multiply robust estimation of the average treatment effect with missing outcomes-Reference-Cited by-同舟云学术

Multiply robust estimation of the average treatment effect with missing outcomes

Published:2022-11-23 Issue: Volume: Page:1-17
ISSN:0094-9655
Container-title:Journal of Statistical Computation and Simulation
language:en
Short-container-title:Journal of Statistical Computation and Simulation

Author:

Wei Kecheng¹,Qin Guoyou¹²^ORCID,Zhang Jiajia³,Sui Xuemei⁴^ORCID

Affiliation:

1. Department of Biostatistics, School of Public Health, and The Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, People's Republic of China

2. Shanghai Institute of Infectious Disease and Biosecurity, Shanghai, People's Republic of China

3. Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA

4. Department of Exercise Science, University of South Carolina, Columbia, SC, USA

Funder

National Natural Science Foundation of China

Shanghai Municipal Science and Technology Major Project

Publisher

Informa UK Limited

Subject

Applied Mathematics,Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/00949655.2022.2143501

Reference31 articles.

1. The central role of the propensity score in observational studies for causal effects

2. Causal Inference for Statistics, Social, and Biomedical Sciences

3. Model-Based Direct Adjustment

4. Variance reduction in the inverse probability weighted estimators for the average treatment effect using the propensity score

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