|dc.description.abstract||Substantial investments are being made in health research to support the conduct of large cohort studies with the objective of improving understanding of the relationships between diverse features (e.g. exposure to toxins, genetic biomarkers, demographic variables) and disease incidence, progression, and mortality. Longitudinal cohort studies are commonly used to study life history processes, that is patterns of disease onset, progression, and death in a population. While primary interest often lies in estimating the effect of some factor on a simple time-to-event outcome, multistate modelling offers a convenient and powerful framework for the joint consideration of disease onset, progression, and mortality, as well as the effect of one or more covariates on these transitions.
Longitudinal studies are typically very costly, and the complexity of the follow-up scheme is often not fully considered at the design stage, which may lead to inefficient allocation of study resources and/or underpowered studies. In this thesis, several aspects of study design are considered to guide the design of complex longitudinal studies, with the general aim being to obtain efficient estimates of parameters of interest subject to cost constraints. Attention is focused on a general $K$ state model where states $1, \ldots, K-1$ represent different stages of a chronic disease and state $K$ is an absorbing state representing death.
In Chapter 2, we propose an approach to design efficient tracing studies to mitigate the loss of information stemming from attrition, a common feature of prospective cohort studies. Our approach exploits observed information on state occupancy prior to loss-to-followup, covariates, and the time of loss-to-followup to inform the selection of individuals to be traced, leading to more judicious allocation of resources. Two settings are considered. In the first there are only constraints on the expected number of individuals to be traced, and in the second the constraints are imposed on the expected cost of tracing. In the latter, the fact that some types of data may be more costly to obtain via tracing than other types of data is dealt with.
In Chapter 3, we focus on two key aspects of longitudinal cohort studies with intermittent assessments: sample size and the frequency of assessments. We derive the Fisher information as the basis for studying the interplay between these factors and to identify features of minimum-cost designs to achieve desired power. Extensions which accommodate the possibility of misclassification of disease status at the intermittent assessments times are developed. These are useful to assess the impact of imperfect screening or diagnostic tests in the longitudinal setting.
In Chapter 4, attention is turned to state-dependent sampling designs for prevalent cohort studies. While incident cohorts involve recruiting individuals before they experience some event of interest (e.g. onset of a particular disease) and prospectively following them to observe this event, prevalent cohorts are obtained by recruiting individuals who have already experienced this event at some point in the past. Prevalent cohort sampling yields length-biased data which has been studied extensively in the survival setting; we demonstrate the impact of this in the multistate setting. We start with observation schemes in which data are subject to left- or right-truncation in the failure-time setting. We then generalize these findings to more complex multistate models. While the distribution of state occupancy at recruitment in a prevalent cohort sample may be driven by the prevalences in the population, we propose approaches for state-dependent sampling at the design stage to improve efficiency and/or minimize expected study cost.
Finally, Chapter 5 features an overview of the key contributions of this research and outlines directions for future work.||en