AbstractsBiology & Animal Science

The statistical evaluation of diagnostic tests may be affected by several potential biases. These biases include those caused by a study design that results in a non-representative sub-sample who are further verified by the reference test (verification bias), and those caused by the absence of a definitive diagnostic test (gold standard test) for many diseases and conditions. In practice, an imperfect reference test is often assumed to be a perfect gold standard, potentially resulting in a large bias. Both Bayesian and frequentist methods have been proposed to adjust for each of these biases independently. To our knowledge, there is no Bayesian solution for that adjusts for both of these biases simultaneously. The objective of this thesis is to present a Bayesian method for the evaluation of diagnostic tests when both of these potential biases may be operating simultaneously. We develop a likelihood function that models both sources of bias, and suggest convenient prior distributions that simplify deriving posterior distributions. The models are based on dichotomous test results and the parameters of interest are estimated using a Gibbs sampler. Using both simulated and real data examples, we demonstrate that the method presented here can correct the verification bias even when a perfect gold standard test does not exist.