Type of Document	Dissertation
Author	Royal-Thomas, Tamika Y. N.
URN	etd-07052011-180845
Title	Interrelating of Longitudinal Processes: An Empirical Example
Degree	Doctor of Philosophy
Department	Statistics, Department of
Advisory Committee	Advisor Name Title Daniel McGee Committee Chair Clive Osmond Committee Member Debajyoti Sinha Committee Member Xufeng Niu Committee Member Cathy Levenson University Representative
Keywords	Principal Component Cardiovascular Fetal Origins Pseudolikelihood Linear Mixed Model Longitudinal
Date of Defense	2011-05-16
Availability	unrestricted
Abstract The Barker Hypothesis states that maternal and `in utero' attributes during pregnancy affects a child's cardiovascular health throughout life. We present an analysis of a unique longitudinal dataset from Jamaica that consists of three longitudinal processes: (i) Maternal longitudinal process- Blood pressure and anthropometric measurements at seven time-points on the mother during pregnancy. (ii) In Utero measurements - Ultrasound measurements of the fetus taken at six time-points during pregnancy. (iii) Birth to present process - Children's anthropometric and blood pressure measurements at 24 time-points from birth to 14 years. A comprehensive analysis of the interrelationship of these three longitudinal processes is presented using joint modeling for multivariate longitudinal profiles. We propose a new methodology of examining child's cardiovascular risk by extending a current view of likelihood estimation. Joint modeling of multivariate longitudinal profiles is done and the extension of the traditional likelihood method is utilized in this paper and compared to the maximum likelihood estimates. Our main goal is to examine whether the process in mothers predicts fetal development which in turn predicts the future cardiovascular health of the children. One of the difficulties with `in utero' and early childhood data is that certain variables are highly correlated and so using dimension reduction techniques are quite applicable in this scenario. Principal component analysis (PCA) is utilized in creating a smaller dimension of uncorrelated data which is then utilized in a longitudinal analysis setting. These principal components are then utilized in an optimal linear mixed model for longitudinal data which indicates that in utero and early childhood attributes predicts the future cardiovascular health of the children. This dissertation has added a body of knowledge to developmental origins of adult diseases and has supplied some significant results while utilizing a rich diversity of statistical methodologies.
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