Design and Analysis of Studies Assessing Exposure Effects in Complex Settings

Abstract

Understanding and efficiently estimating the effects of exposures on health outcomes is a fundamental goal in public health research. The accuracy and efficiency of exposure effect estimates are heavily influenced by study design and the statistical methodologies employed for analyses. This thesis consists of three projects where statistical methods are developed to address unique challenges in the estimation of exposure effects in diverse and complex settings.

The first project concerns causal inference regarding the effects of multiple exposure variables and their interactions which require modeling the joint distribution of exposures given pertinent confounding variables. This modeling can be carried out via a second-order regression model. Chapter 2 presents methodologies using regression adjustment and inverse weighting to investigate the asymptotic bias of estimators when the dependence model for the generalized propensity score incorrectly assumes conditional independence of exposures or is based on a naive dependence model which does not accommodate the effect of confounders on the conditional association of exposures. We also consider the problem of a semi-continuous bivariate exposure that arises when the population is made up of a sub-population of unexposed individuals and a sub-population of exposed individuals in whom the level of exposure can be quantified. We propose a two-stage estimation technique to study the effects of prenatal alcohol exposure, and specifically the effects of drinking frequency and intensity on childhood cognition in Chapter 2.

The second project focuses on plasma donation, highlighting the importance of rigorous device safety evaluations for donors. Complications arise from the fact that outcomes on successive donations from the same donor are not independent, adverse event rates are extremely rare, and there is substantial heterogeneity in the propensity for donors to donate over time. Chapter 3 introduces a statistical framework for designing superiority and non-inferiority trials to assess the safety of a new donation device compared to the standard one. A unique feature is that the number of donations per donor varies substantially so some individuals contribute more information and others less. Historical data on the donation rate and variation in the donation rate (heterogeneity) across donors, the adverse event rate, and the serial dependence in adverse events provide the necessary information to plan for the duration of accrual and follow-up periods. Specifically the sample size formula is derived to ensure power requirements are met when analyses are based on generalized estimating equations and robust variance estimation. The complexity of recruiting donors from a heterogeneous and dynamic population of donors means that it is challenging to characterize the rate at which information is acquired on treatment effects over the course of the study. Strategies for interim monitoring based on group sequential designs using alpha spending functions are developed based on a robust covariance matrix for estimates of treatment effect over successive analyses. The design of a plasma donation study is illustrated in Chapter 3 aiming to investigate the safety of a new device with the outcome being serious hypotensive adverse events.

Many chronic diseases can be naturally characterized using multistate models. Longitudinal cohorts and registry studies of chronic diseases typically recruit and follow individuals to record data on the nature and timing of disease progression. In many cases the exact transition times between disease states are not observed directly, but the state occupied at each clinic visit is known. Such studies also routinely collect and store serum samples at intermittent clinic visits. The final project explores the design and analysis of two-phase studies for evaluating the effect of a biomarker of interest. We consider the design of two-phase studies in Chapter 4 aimed at selecting individuals for biospecimen assays to measure biomarkers of interest and estimate their association with disease progression through intensity-based modeling. Likelihood-based and estimating function approaches are developed and the efficiency gains from score residual-dependent sampling strategies are investigated for joint models of the biomarker and disease progression processes. The efficiency of these different frameworks is investigated, and the methods are applied to a study investigating the association between the HLA-B27 marker and joint damage in psoriatic arthritis patients.

The thesis concludes with some topics of future research in Chapter 5.