Bayesian Modeling and Statistical Inference for Longitudinal Diagnostic Outcomes

Diagnostic screening involves testing humans or animals for the presence of disease or infection. The goals of diagnostic screening may include: quantifying the performance of an imperfect screening test, diagnosing subjects, and estimating disease prevalence. We develop a novel model for joint longitudinal diagnostic screening outcomes in the no-gold standard case. We consider the situation where two tests are repeatedly administered to each subject -- one yielding a continuous response and the other a binary response. For infected subjects, we assume the existence of a changepoint corresponding to time of infection and posit appropriate changes to the model thereafter. In addition to diagnosis, we are able to estimate sensitivity and specificity of the binary test and also determine the effect of time since diagnosis on sensitivity of a test that is based on the dichotomized continuous outcome.