PREDICTING THE ONSET OF ALZHEIMERS-DISEASE USING BAYES THEOREM

Bayes' theorem describes the effect of new information (e.g., a test r esult) on the probability of an outcome (e.g., a disease). Likelihood ratios for separate tests can be combined to assess the joint effect o f 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 Te st, and Raven's Progressive Matrices) combined with age and family his tory of dementia. A total of 1,454 subjects who had been recruited int o the Medical Research Council Elderly Hypertension Trial between 1983 and 1985 completed cognitive tests at entry to the trial (when they w ere without signs of dementia) and 1 month later. Their dementia statu s was ascertained in 1990-1991. The test package identified 52% of Alz heimer's disease cases with a 9% false-positive rate or 90% of Alzheim er'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 apol ipoprotein E e4 status, which is a powerful risk factor for late-onset Alzheimer's disease, as a likelihood ratio approach to the prospectiv e identification of Alzheimer's disease cases. This approach could be followed by ethically sound trials of new therapeutic agents for subje cts who have a high probability of developing Alzheimer's disease.