Methods for joint modeling in the longitudinal data analysis

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dc.contributor.advisorPark, DoHwan
dc.contributor.authorzhu, shijun
dc.contributor.departmentMathematics and Statistics
dc.contributor.programStatistics
dc.date.accessioned2022-02-09T15:52:54Z
dc.date.available2022-02-09T15:52:54Z
dc.date.issued2020-01-01
dc.description.abstractWe study statistical methods for joint modeling that accommodates the correlations among multiple longitudinal outcomes and the time to recurrent events. We first propose the multivariate latent growth curve modeling with conditional indirect effects under the structural equation modeling framework to assess the longitudinal moderated mediation effects. We conduct an extensive simulation study to evaluate the statistical performance of the complex model in terms of bias and coverage probability of parameter estimation, and statistical power with regard to R2, effect sizes of mediation effects and moderated mediation effects, sample size, and number of measurement occasions. We apply this model to analyze a real data. In the second topic, we develop another joint modeling to analyze bivariate longitudinal outcomes and time to recurrent events data. We combine the bivariate normal mixed effect model and the frailty model by including the multivariate normal random variables, which account for the dependence among the repeated measures and the dependence between two longitudinal outcomes and recurrent events. We use nonparametric maximum likelihood estimation (NPMLE) to estimate the parameters. The numerical algorithm of expectation-maximization (EM) algorithm was used to compute the NPMLEs and their variance estimators. The results from the simulation study show that the NPMLEs are noticeably unbiased and the standard error estimators well reflect the true variations of the proposed estimators. The coverage probabilities are in the reasonable range. The results from the joint models are better than the individual models. We further extend the joint model to address the dependence between the recurrent events with each individual by including an additional random effect to model the correlations. We use the similar NPMLE approach to estimate the parameters via EM algorithm. Simulation study shows NPMLE and variance estimation are unbiased even with the higher dimensional random effects included in the model. The joint model is applied to a real data -- Urea Cycle Disorders study.
dc.formatapplication:pdf
dc.genredissertations
dc.identifierdoi:10.13016/m2vezz-izw2
dc.identifier.other12371
dc.identifier.urihttp://hdl.handle.net/11603/24213
dc.languageen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics and Statistics Department Collection
dc.relation.ispartofUMBC Theses and Dissertations Collection
dc.relation.ispartofUMBC Graduate School Collection
dc.relation.ispartofUMBC Student Collection
dc.sourceOriginal File Name: zhu_umbc_0434D_12371.pdf
dc.subjectjoint modeling
dc.subjectlongitudinal data analysis
dc.subjectmediation
dc.subjectmoderation
dc.titleMethods for joint modeling in the longitudinal data analysis
dc.typeText
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