191195 Bayesian Modeling and Statistical Inference for Longitudinal Diagnostic Outcomes

Tuesday, October 28, 2008: 5:10 PM

Wesley Johnson, PhD , Department of Statistics, University of California - Irvine, Irvine, CA
Michelle Norris, PhD , Department of Mathematics and Statistics, California State University, Sacramento, Sacramento, CA
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.

Learning Objectives:
Observe new statistical technique.

Keywords: Statistics, Outcomes Research

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: Professor of Statistics
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.