@article{ec3f0d30a28842c29c9855be8240aceb,
title = "A multiple robust propensity score method for longitudinal analysis with intermittent missing data",
abstract = "Longitudinal data are very popular in practice, but they are often missing in either outcomes or time-dependent risk factors, making them highly unbalanced and complex. Missing data may contain various missing patterns or mechanisms, and how to properly handle it for unbiased and valid inference still presents a significant challenge. Here, we propose a novel semiparametric framework for analyzing longitudinal data with both missing responses and covariates that are missing at random and intermittent, a general and widely encountered situation in observational studies. Within this framework, we consider multiple robust estimation procedures based on innovative calibrated propensity scores, which offers additional relaxation of the misspecification of missing data mechanisms and shows more satisfactory numerical performance. Also, the corresponding robust information criterion on consistent variable selection for our proposed model is developed based on empirical likelihood-based methods. These advocated methods are evaluated in both theory and extensive simulation studies in a variety of situations, showing competing properties and advantages compared to the existing approaches. We illustrate the utility of our approach by analyzing the data from the HIV Epidemiology Research Study.",
author = "Chixiang Chen and Biyi Shen and Aiyi Liu and Rongling Wu and Ming Wang",
note = "Funding Information: Wang's research was partially supported by Grant UL1 TR002014 and KL2 TR002015 from the National Center for Advancing Transnational Sciences (NCATS) and was also funded, in part, under a grant from the Pennsylvania Department of Health using Tobacco CURE Funds. Research of Liu was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). Data from the HERS were collected under from the U.S. Centers for Disease Control and Prevention, and the authors especially thank the HERS participants and the HERS Research Group. The content is solely the responsibility of the authors and does not represent the official views of the National Institute of Health, the U.S. Centers for Disease Control and Prevention and other affiliated institutes, and also the Department specially disclaims responsibility for any analyses, interpretations, or conclusions. Funding Information: Wang's research was partially supported by Grant UL1 TR002014 and KL2 TR002015 from the National Center for Advancing Transnational Sciences (NCATS) and was also funded, in part, under a grant from the Pennsylvania Department of Health using Tobacco CURE Funds. Research of Liu was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). Data from the HERS were collected under from the U.S. Centers for Disease Control and Prevention, and the authors especially thank the HERS participants and the HERS Research Group. The content is solely the responsibility of the authors and does not represent the official views of the National Institute of Health, the U.S. Centers for Disease Control and Prevention and other affiliated institutes, and also the Department specially disclaims responsibility for any analyses, interpretations, or?conclusions. Publisher Copyright: {\textcopyright} 2020 The International Biometric Society",
year = "2021",
month = jun,
doi = "10.1111/biom.13330",
language = "English (US)",
volume = "77",
pages = "519--532",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley-Blackwell",
number = "2",
}