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A Review and Comparison of Models and Estimation Methods for Multivariate Longitudinal Data of Mixed Scale Type

Codd, Casey
http://rave.ohiolink.edu/etdc/view?acc_num=osu1398686513

Abstract Details

2014, Doctor of Philosophy, Ohio State University, Psychology.
Models for the joint analysis of multiple outcome variables which are of possibly different scale types are useful because they allow researchers to answer questions related to the association between the trajectories of several variables and also provide a way to evaluate the change in the association between variables over time. There are several types of models that can handle multivariate longitudinal data, but one common approach involves using generalized linear mixed models with correlated random effects. This type of model is quickly growing in popularity in fields such as medicine and biostatistics, and the potential applications in the behavioral sciences are also quite broad. The limiting feature has been that estimation of the model parameters involves integration over the random effects in order to obtain the marginal distribution of the data. In this dissertation, a review of several widely-available estimation methods and models for multivariate longitudinal data is provided. To evaluate the performance of the estimation methods within the multivariate generalized linear mixed model framework, a simulation study was conducted. The particular estimation methods of interest were adaptive Gaussian quadrature (AGQ), Laplace approximation (LA), penalized quasi-likelihood (PQL), and marginal quasi-likelihood (MQL). Results indicated that although AGQ and LA typically estimate the parameter estimates with less bias, PQL and MQL tend to produce more stable estimates for the covariance matrix of random effects. Furthermore, even though PQL performed quite poorly in many conditions, the bias of parameter estimates tended to decrease as the correlation between the random effects increased. Data from the National Longitudinal Study of Youth are used to illustrate the applicability of the multivariate generalized linear mixed model to behavioral data. A summary of the major findings in the literature review and also of numerical studies is given.
Robert Cudeck, Ph.D. (Advisor)
Michael Edwards, Ph.D. (Committee Member)
Minjeong Jeon, Ph.D. (Committee Member)
144 p.
Quantitative Psychology
generalized linear mixed model; longitudinal data analysis; mixed model

Recommended Citations

Citations

  • Codd, C. (2014). A Review and Comparison of Models and Estimation Methods for Multivariate Longitudinal Data of Mixed Scale Type [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398686513

    APA Style (7th edition)

  • Codd, Casey. A Review and Comparison of Models and Estimation Methods for Multivariate Longitudinal Data of Mixed Scale Type. 2014. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1398686513.

    MLA Style (8th edition)

  • Codd, Casey. "A Review and Comparison of Models and Estimation Methods for Multivariate Longitudinal Data of Mixed Scale Type." Doctoral dissertation, Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398686513

    Chicago Manual of Style (17th edition)

osu1398686513
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This open access ETD is published by The Ohio State University and OhioLINK.