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dc.contributor.advisorLim, Hyun Ja June
dc.creatorCheng, Yanzhao
dc.date.accessioned2021-11-01T17:50:19Z
dc.date.available2021-11-01T17:50:19Z
dc.date.created2021-09
dc.date.issued2021-11-01
dc.date.submittedSeptember 2021
dc.identifier.urihttps://hdl.handle.net/10388/13665
dc.description.abstractABSTRACT Traditionally, two kinds of methods are applied in trajectory analysis: 1) hierarchical modeling based on a multilevel structure, or 2) latent growth curve modeling (LGCM) based on a covariance structure (Raudenbush & Bryk, 2002; Bollen & Curran, 2006). However, this thesis used a third trajectory analysis method: group-based trajectory modeling (GBTM). GBTM was an extension of the finite mixture modeling (FMM) method that has been widely used in various fields of trajectory analysis in the last 25 years (Nagin & Odgers, 2010). GBTM was able to detect unobserved subgroups based on the multinomial logit function (Nagin, 1999). As an extended form of FMM, GBTM parameters could be estimated using maximum likelihood estimation (MLE) procedures. Since FMMs had no closed-form solution to the maximum likelihood, the Expectation-Maximization (EM) algorithm would often be applied to find maximized likelihood (Schlattmann, 2009). However, GBTM used a different optimization method called the Quasi-Newton procedure to perform the maximization. This thesis studied both GBTM with a single outcome and trajectory modeling with multiple outcomes. Nagin constructed two extended trajectory models that can involve multiple outcomes. Group-based dual trajectory modeling (GBDTM) deals with two outcomes combined with comorbidity or heterotypic continuity, while group-based multi-trajectory modeling (GBMTM) could include more than two outcomes in one model with the same subgroup weights among the outcomes (Nagin, 2005; Nagin, Jones, Passos, & Tremblay, 2018; Nagin & Tremblay, 2001). The methodology was applied to the Korea health panel survey (KHPS) data, which included 3983 individuals who were 65 years old or older at the baseline. GBTM, GBDTM, and GBMTM were three approaches performed with two binary longitudinal outcomes - depression and anxiety. GBDTM was selected as the best model with this data set because it is more flexible than GBMTM when handling group membership, and unlike GBTM, GMDTM addressed the interrelationship between the outcomes based on conditional probability. Four iii depression trajectories were identified across eight years of follow-up: “low-flat” (n = 3641; 87.0%), “low-to-middle” (n = 205; 8.8%), “low-to-high” (n = 33; 1.3%) and “high-curve” (n = 104; 2.8%). Also, four anxiety trajectories were identified with: “low-flat” (n =3785; 92.5%), “low-to-middle” (n = 96; 4.7%), “high-to-low” (n =89; 2.2%) and “high-curve” (n = 13; 0.6%) trajectory groups. Female sex, the presence of more than three chronic diseases, and income-generating activity were significant risk factors for depression trajectory groups. Anxiety trajectory groups had the same risk factors except for the presence of more than three chronic diseases. To further study the GBTM, GBDTM and GBMTM approach, the simulation study was also performed based on two correlated repeatedly measured binary outcomes. Compared based on these two outcomes with different correlation levels (ρ = 0.1, 0.2, 0.4, 0.6). GBDTM was always a better model than GBTM when we were interested in the association between the two outcomes. GBMTM could be used instead of GBDTM when the correlation coefficients between two longitudinal outcomes were high.
dc.format.mimetypeapplication/pdf
dc.subjectTrajectory analysis, Mental health
dc.titleMULTIVARIATE FINITE MIXTURE GROUP-BASED TRAJECTORY MODELING WITH APPLICATION TO MENTAL HEALTH STUDIES
dc.typeThesis
dc.date.updated2021-11-01T17:50:20Z
thesis.degree.departmentSchool of Public Health
thesis.degree.disciplineBiostatistics
thesis.degree.grantorUniversity of Saskatchewan
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)
dc.type.materialtext
dc.contributor.committeeMemberLi, Longhai
dc.contributor.committeeMemberJanzen, Bonnie
dc.contributor.committeeMemberBalbuena, Lloyd Cenon
dc.contributor.committeeMemberFeng, Xin
dc.contributor.committeeMemberBoudreau, Christian
dc.creator.orcid0000-0001-5940-7831


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