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Longitudinal Regression Analysis Using Varying Coefficient Mixed Effect Model

Al-Shaikh, Enas
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1342543464

Abstract Details

2012, PhD, University of Cincinnati, Medicine: Biostatistics (Environmental Health).
Linear and nonlinear mixed models are very powerful techniques for modeling the relationship between a response variable and covariates and for handling the within-subject correlations in longitudinal data. For many applications in real life, however, it is difficult to find the proper parametric model to fit the data. Therefore, the adequacy of the model assumptions and the potential consequences of model misspecifications on the analysis under the classical linear model framework are questionable. Thus, it is important to increase the flexibility of linear regression models and to relax the conditions imposed on traditional parametric models to explore the hidden structure. The varying coefficient model (VCM), which was proposed by Hastie and Tibshirani (1993), provides a versatile and flexibale analysis tool for relating longitudinal responses to longitudinal predictors. Specically, this approach provides a novel representation of varying coefficient functions through suitable covariance of the underlying stochastic processes, which is particularly advantageous for sparse and irregular designs, as often encountered in longitudinal studies. In this dissertation, we hypothesized that varying coefficient mixed effect model (VCMEM) accurately predict, explore and address the relationship between four different covariates and the antigen level of MsgC using penalized spline smoothing technique. The longitudinal data were obtained from the Multicenter AIDS Cohort Study (MACS). We have two specific aims to test this hypothesis. The first aim is to fit VCMEM to MACS data, where the variable antigen level of MsgC is continuous. The second aim is to perform goodness of fit test to investigate the significance of the model covariates in VCMEM in the first aim using bootstrap techniques. We focused on fitting the VCMEM for the MACS data, where both fixed and random effects were modeled non-parametrically with P-spline smoothing. This allows us to explore how the effects of the covariates such as Prophylaxis treatment vary with time. In addition, mixed effect model captured the features of the individual profiles. Finally, we successfully demonstrated that the use of VCMEM for the MACS data provided an accurate analysis that describes the dynamic and structure of change and addresses the relationship between covariates such as the age at first AIDS define illness, at least one PcP episode before the death, taking Prophylaxis and the geographical location on the antigen level of MsgC. Based on our results, we concluded that the VCMEM fits the data better than the Linear mixed effect model (LME) models.
Linda Levin, PhD (Committee Chair)
Charles Ralph Buncher, ScD (Committee Member)
Paul Succop, PhD (Committee Member)
Peter Walzer, MD MSc (Committee Member)
107 p.
Biostatistics
varying coefficient functions; longitudinal data; penalized spline smoothing; ; ; ;

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Citations

  • Al-Shaikh, E. (2012). Longitudinal Regression Analysis Using Varying Coefficient Mixed Effect Model [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1342543464

    APA Style (7th edition)

  • Al-Shaikh, Enas. Longitudinal Regression Analysis Using Varying Coefficient Mixed Effect Model. 2012. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1342543464.

    MLA Style (8th edition)

  • Al-Shaikh, Enas. "Longitudinal Regression Analysis Using Varying Coefficient Mixed Effect Model." Doctoral dissertation, University of Cincinnati, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1342543464

    Chicago Manual of Style (17th edition)

ucin1342543464
492
© 2012, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.