A multiple robust propensity score method for longitudinal analysis with intermittent missing data-Reference-Cited by-同舟云学术

A multiple robust propensity score method for longitudinal analysis with intermittent missing data

Published:2020-07-25 Issue:2 Volume:77 Page:519-532
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biometrics

Author:

Chen Chixiang¹^ORCID,Shen Biyi¹,Liu Aiyi²^ORCID,Wu Rongling¹,Wang Ming¹^ORCID

Affiliation:

1. Division of Biostatistics and Bioinformatics Department of Public Health Sciences Penn State College of Medicine Hershey Pennsylvania

2. Biostatistics and Bioinformatics Branch National Institute of Child Health and Human Development NIH Bethesda Maryland

Funder

Pennsylvania Department of Health

National Center for Advancing Translational Sciences

Eunice Kennedy Shriver National Institute of Child Health and Human Development

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13330

Reference30 articles.

1. Doubly Robust Estimation in Missing Data and Causal Inference Models

2. Multiple imputation of discrete and continuous data by fully conditional specification

3. Weighted Generalized Estimating Functions for Longitudinal Response and Covariate Data That Are Missing at Random

4. Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates

5. Empirical‐likelihood‐based criteria for model selection on marginal analysis of longitudinal data with dropout missingness

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