Danping Liu1, Edwina H Yeung2, Alexander C McLain3, Yunlong Xie4, Germaine M Buck Louis5, Rajeshwari Sundaram1. 1. Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health. 2. Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD. 3. Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC. 4. Glotech Inc., Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health. 5. Division of Intramural Population Health Research, Office of the Director, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD.
Abstract
BACKGROUND: Imperfect follow-up in longitudinal studies commonly leads to missing outcome data that can potentially bias the inference when the missingness is nonignorable; that is, the propensity of missingness depends on missing values in the data. In the Upstate KIDS Study, we seek to determine if the missingness of child development outcomes is nonignorable, and how a simple model assuming ignorable missingness would compare with more complicated models for a nonignorable mechanism. METHODS: To correct for nonignorable missingness, the shared random effects model (SREM) jointly models the outcome and the missing mechanism. However, the computational complexity and lack of software packages has limited its practical applications. This paper proposes a novel two-step approach to handle nonignorable missing outcomes in generalized linear mixed models. We first analyse the missing mechanism with a generalized linear mixed model and predict values of the random effects; then, the outcome model is fitted adjusting for the predicted random effects to account for heterogeneity in the missingness propensity. RESULTS: Extensive simulation studies suggest that the proposed method is a reliable approximation to SREM, with a much faster computation. The nonignorability of missing data in the Upstate KIDS Study is estimated to be mild to moderate, and the analyses using the two-step approach or SREM are similar to the model assuming ignorable missingness. CONCLUSIONS: The two-step approach is a computationally straightforward method that can be conducted as sensitivity analyses in longitudinal studies to examine violations to the ignorable missingness assumption and the implications relative to health outcomes.
BACKGROUND: Imperfect follow-up in longitudinal studies commonly leads to missing outcome data that can potentially bias the inference when the missingness is nonignorable; that is, the propensity of missingness depends on missing values in the data. In the Upstate KIDS Study, we seek to determine if the missingness of child development outcomes is nonignorable, and how a simple model assuming ignorable missingness would compare with more complicated models for a nonignorable mechanism. METHODS: To correct for nonignorable missingness, the shared random effects model (SREM) jointly models the outcome and the missing mechanism. However, the computational complexity and lack of software packages has limited its practical applications. This paper proposes a novel two-step approach to handle nonignorable missing outcomes in generalized linear mixed models. We first analyse the missing mechanism with a generalized linear mixed model and predict values of the random effects; then, the outcome model is fitted adjusting for the predicted random effects to account for heterogeneity in the missingness propensity. RESULTS: Extensive simulation studies suggest that the proposed method is a reliable approximation to SREM, with a much faster computation. The nonignorability of missing data in the Upstate KIDS Study is estimated to be mild to moderate, and the analyses using the two-step approach or SREM are similar to the model assuming ignorable missingness. CONCLUSIONS: The two-step approach is a computationally straightforward method that can be conducted as sensitivity analyses in longitudinal studies to examine violations to the ignorable missingness assumption and the implications relative to health outcomes.
Authors: Edwina H Yeung; Rajeshwari Sundaram; Erin M Bell; Charlotte Druschel; Christopher Kus; Akhgar Ghassabian; Scott Bello; Yunlong Xie; Germaine M Buck Louis Journal: JAMA Pediatr Date: 2016-03 Impact factor: 16.193
Authors: Germaine M Buck Louis; Mary L Hediger; Erin M Bell; Christopher A Kus; Rajeshwari Sundaram; Alexander C McLain; Edwina Yeung; Elaine A Hills; Marie E Thoma; Charlotte M Druschel Journal: Paediatr Perinat Epidemiol Date: 2014-03-25 Impact factor: 3.980
Authors: Maureen Murdoch; Barbara A Clothier; Timothy J Beebe; Ann K Bangerter; Siamak Noorbaloochi Journal: BMC Med Res Methodol Date: 2022-03-06 Impact factor: 4.615