An epidemiological study often uses a two-phase design to estimate the prevalence rate of a mental disease. In a two-phase design study, the first phase assesses a large sample with an inexpensive screening test, and then the second phase selects a subsample for a more expensive diagnostic evaluation. Furthermore, disease status may not be ascertained for all subjects who are selected for disease verification because some subjects are unable to be clinically assessed, while others may refuse. Since not all screened subjects are selected for diagnostic assessments, there is potential for verification bias. In this paper, we propose the maximum likelihood (ML) and bootstrap methods to correct for verification bias for estimating and comparing the prevalence rates under the missing-at-random (MAR) assumption for the verification mechanism. We also propose a method to test this MAR assumption. Finally, we apply our methods to a large-scale prevalence study of dementia disorders.
|Original language||English (US)|
|Number of pages||12|
|Journal||Statistics in Medicine|
|State||Published - May 30 1999|
ASJC Scopus subject areas
- Statistics and Probability