Longitudinal analysis techniques in epidemiology : how prospective cohort data can be used to understand pathways to cardiometabolic disease outcomes : findings from the cardiovascular risk in young Finns study

The overall contribution of this thesis has been the application and development of growth modeling methods for analysing continuous longitudinal data arising from long-term observational studies in epidemiology, with a special emphasis on how these techniques can be used to investigate pathways leading to important cardiometabolic risk factors or deleterious cardiovascular disease outcomes in adulthood. These risk factors (obesity, dyslipidemia and hypertension) and clinical outcomes (pre-atherosclerosis, and type 2 diabetes mellitus) are important because they are associated with increased risk of developing heart disease and stroke. This general aim was addressed in three parts: 1. First, analytical strategies and key issues relative to the application of growth curve modeling methods to continuous non-linear response data were identified, reviewed and compiled. In this phase, specific emphasis was placed on the technical considerations of data arising from cohort sequential studies where there were less than 10 time points per person collected across the life course, and where the time between sequential measures is not balanced between participants. This aim was addressed in Chapter 2, detailed below. 2. Second, growth curve modeling theory was tailored and extended to develop a Bayesian trajectory divergence‚ÄövÑvp method in Chapter 4. This method allows the identification of the point or age, in the life course when participants who develop important health outcomes later in life begin to diverge in their nonlinear trajectories of continuous modifiable risk factors, compared with those who remain healthy. 3. Third, the reviewed and developed growth curve methodologies in part one were applied to model and analyse the longitudinal trajectories of a number of continuous measures of cardiometabolic risk. Data from the Cardiovascular Risk in Young Finns Study (YFS) was used. The Young Finns Study is an accelerated prospective cohort study that has collected cardiometabolic risk factor data in a large sample of Finnish participants from childhood to mid-adulthood across eight waves of follow-up over the course of 31 years. These analyses were written as a series of 4 original papers included in Chapters 3 to 6, which have been (or will be) submitted to journals.