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Model and Inference Issues Related to Exposure-Disease Relationships

Tuesday, April 22, 2014 - 11:00
Li Xing, PhD Student, UBC Statistics
Room 4192, Earth Sciences Building (2207 Main Mall)

We work on two issues related to exposure-disease relationships. Firstly we build a Bayesian hierarchical model for relating disease to a potentially harmful exposure, using data from studies in occupational epidemiology, and compare our method with the traditional group-based exposure assessment 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 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 group-based exposure assessment method may not be suitable to use.

Secondly, the shape of the relationship between a continuous exposure variable and a binary disease variable is often central to epidemiologic investigations. We investigates a number of issues surrounding inference and the shape of the 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. This is done both in the case of using a model acknowledging uncertainty about the shape parameter and in the case of using a simple model ignoring this uncertainty. 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.