Casey's problem: Interpreting and evaluating a new test

Casey, the newborn daughter of one of the authors of this paper, received a positive result on an experimental medical screening test, indicating that she may lack an enzyme required to digest certain fats. The interpretation of this test result was complicated by uncertainty about the false-positive rate for the test - this was the first positive reading - and the prevalence of the medical condition. We used a simple Bayesian model to help assess the probability that Casey actually had the enzyme deficiency and to help better understand the role and value of this screening test. The model we used and, more generally, our style of analysis could also be used with other new diagnostic tests, such as tests used in manufacturing and environmental contexts as well as other medical situations.

Duke Scholars

Author Robert L. Winkler Fuqua School of Business