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Evaluating the performance of hypothesis testing in case-control studies with exposure misclassification, using frequentist and Bayesian techniques

University of British Columbia

In epidemiologic studies, measurement error in the exposure variable can have large effects on the power of hypothesis testing for detecting the impact of exposure in the development of a disease. As it distorts the structure of data, more uncertainty is associated with the inferential procedure involving such exposure variables. The underlying theme of this thesis is the adjustment for misclassification in the hypothesis testing procedure. We consider problems involving a correctly measured binary response and a misclassified binary exposure variable in a retrospective case-control scenario. We account for misclassification error via validation data under the assumption of non-differential misclassification. The objective here is to develop a test to check whether the exposure prevalence rates of cases and controls are the same or not, under the frequentist and Bayesian point of view. To evaluate the test developed under the Bayesian approach, we compare that with an equivalent test developed under the frequentist approach. Both these approaches were developed in two different settings: in the presence or absence of validation data, to evaluate whether there is any gain in hypothesis testing for having such validation data. The frequentist approach involves the likelihood ratio test, while the Bayesian test is developed from posterior distribution generated by a mixed MCMC algorithm and a normal prior under realistic assumptions. The comparison between these two approaches is conducted using different simulated scenarios, as well as two real case-control studies having partial validation (internal) data. Different scenarios include settings with varying sensitivity and specificity, sample sizes, exposure prevalence and proportion of unvalidated and validated data. One other scenario that was considered is to evaluate the performance under a fixed budgetary constraint. In the scenarios under consideration, we reach the same conclusion from the two hypothesis testing procedures. The simulation study suggests that the adjusted model (with validation data model) is always better than the unadjusted model (without validation data model). However, exception is possible in the fixed budget scenario.

Text

2010-03-24

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/22472

Statistics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/