Predicting the onset of Alzheimer's disease using Bayes' theorem

Abstract

Bayes' theorem describes the effect of new information (e.g., a test result) on the probability of an outcome (e.g., a disease). Likelihood ratios for separate tests can be combined to assess the joint effect of their results on disease probability. This approach has been used to develop a test package for Alzheimer's disease that consists of some simple cognitive tests (Paired Associate Learning Test, Trailmaking Test, and Raven's Progressive Matrices) combined with age and family history of dementia. A total of 1,454 subjects who had been recruited into the Medical Research Council Elderly Hypertension Trial between 1983 and 1985 completed cognitive tests at entry to the trial (when they were without signs of dementia) and 1 month later. Their dementia status was ascertained in 1990-1991. The test package identified 52% of Alzheimer's disease cases with a 9% false-positive rate or 90% of Alzheimer's disease cases with a 29% false-positive rate. The author proposes the use of a similar test package in conjunction with a test for apolipoprotein E e4 status, which is a powerful risk factor for late-onset Alzheimer's disease, as a likelihood ratio approach to the prospective identification of Alzheimer's disease cases. This approach could be followed by ethically sound trials of new therapeutic agents for subjects who have a high probability of developing Alzheimer's disease.