Abstract
A two-phase design has been widely used in epidemiological studies of dementia. The first phase assesses a large sample with screening tests. The second, based on the screening test results and possibly on other observed patient’s factors, selects a subset of the study sample for a more definitive disease verification assessment. In comparing the accuracies of two screening tests in a two-phase study of dementia, inferences are commonly made from a sample of verified cases. The omission of non-verified cases can seriously bias comparison results. To correct for this bias, we derive the maximum likelihood (ML) estimators for the accuracies of two screening tests and their corresponding correlation. The p-values and confidence intervals are computed using the asymptotic normality of the ML estimators. Our method is used to compare the accuracies of two screening tests in a two-phase epidemiological study of dementia. We found that, although the sensitivities of the new and standard screening tests in detecting a diseased subject are not different, the new screening test performs better in detecting a non-diseased subject.