- 期刊
- OpenAccess
Mixed-effect Models for Truncated Longitudinal Outcomes with Nonignorable Missing Data
並列摘要
Mixed effects models are often used for estimating fixed effects and variance components in continuous longitudinal outcomes. An EM based estimation approach for mixed effects models when the outcomes are truncated was proposed by Hughes (1999). We consider the situation when the longitudinal outcomes are also subject to non-ignorable missing in addition to truncation. A shared random effect parameter model is presented where the missing data mechanism depends on the random effects used to model the longitudinal outcomes. Data from the Indianapolis-Ibadan dementia project is used to illustrate the proposed approach.
延伸閱讀
- SANDE, S. Z., SENG, L., LI, J., & AGOSTINO, R. D. (2021). Statistical Learning in Medical Research with Decision Threshold and Accuracy Evaluation. Journal of Data Science, 19(4), 634-657. https://doi.org/10.6339/21-JDS1022
- 黃紀（2008）。Causal Inference and Observational Study: On the Counterfactual Model of Causality。社會科學論叢，2(1)，1-22。https://doi.org/10.30401/RSS.200804_2(1).0001
- Lin, K. H. (2010). A flexible model approach for regression analysis of multivariate interval-censored data [master's thesis, Tamkang University]. Airiti Library. https://doi.org/10.6846/TKU.2010.01057
- Ciou, F. Y. (2022). Model selection criteria based on empirical-likelihood for incomplete longitudinal data [master's thesis, Tamkang University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0002-2106202220162000
- Lin, T. C., & Tsai, G. F. (2018). RANDOM-EFFECTS MIXTURE MODEL WITH AR (1) DEPENDENCE FOR CLASSIFYING LONGITUDINAL CLINICAL DATA. 中國統計學報, 56(1), 7-24. https://www.airitilibrary.com/Article/Detail?DocID=05296528-201803-201803050008-201803050008-7-24