Recoverability and estimation of causal effects under typical multivariable missingness mechanisms
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Publication:6625454
DOI10.1002/bimj.202200326zbMATH Open1547.62551MaRDI QIDQ6625454
S. Ghazaleh Dashti, Jiaxin Zhang, Katherine J. Lee, John B. Carlin, Margarita Moreno-Betancur
Publication date: 28 October 2024
Published in: Biometrical Journal (Search for Journal in Brave)
Cites Work
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- What is meant by ``missing at random?
- On the number of bootstrap simulations required to construct a confidence interval
- Recursive partitioning for missing data imputation in the presence of interaction effects
- Maximum likelihood multiple imputation: faster imputations and consistent standard errors without posterior draws
- A Three-step Method for Choosing the Number of Bootstrap Repetitions
- Statistical Analysis with Missing Data, Third Edition
- A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
- Graphical Models for Processing Missing Data
- Multiple imputation of discrete and continuous data by fully conditional specification
- Evaluation of a three-step method for choosing the number of bootstrap repetitions
- Using simulation studies to evaluate statistical methods
- Propensity scores using missingness pattern information: a practical guide
- A fair comparison of tree-based and parametric methods in multiple imputation by chained equations
- Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non-linear terms
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