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UBC Theses and Dissertations

Model and inference issues related to exposure-disease relationships Xing, Li


The goal of my thesis is to make contributions on some statistical issues related to epidemiological investigations of exposure-disease relationships. Firstly, when the exposure data contain missing values and measurement errors, we build a Bayesian hierarchical model for relating disease to a potentially harmful exposure while accommodating these flaws. The traditional imputation method, called the group-based exposure assessment method, uses the group exposure mean to impute the individual exposure in that group, where the group indicator indicates that the exposure levels tend to vary more across groups and less within groups. We compare our method with the traditional method through simulation studies, a real data application, and theoretical calculation. We focus on cohort studies where a logistic disease model is appropriate and where group exposure means can be treated as fixed effects. The results show a variety of advantages of the fully Bayesian approach, and provide recommendations on situations where the traditional method may not be suitable to use. Secondly, we investigate a number of issues surrounding inference and the shape of the exposure-disease relationship. Presuming that the relationship can be expressed in terms of regression coefficients and a shape parameter, we investigate how well the shape can be inferred in settings which might typify epidemiologic investigations and risk assessment. We also consider a suitable definition of the average effect of exposure, and investigate how precisely this can be inferred. We also examine the extent to which exposure measurement error distorts inference about the shape of the exposure-disease relationship. All these investigations require a family of exposure-disease relationships indexed by a shape parameter. For this purpose, we employ a family based on the Box-Cox transformation. Thirdly, matching is commonly used to reduce confounding due to lack of randomization in the experimental design. However, ignoring measurement errors in matching variables will introduce systematically biased matching results. Therefore, we recommend to fit a trajectory model to the observed covariate and then use the estimated true values from the model to do the matching. In this way, we can improve the quality of matching in most cases.

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