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.