This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 534
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #308318
Title: Estimating accuracy of a diagnostic test with verification bias when the number of observed false negatives is small: a Monte Carlo approach
Author(s): Li Deng*+
Companies: New England College of Optometry
Address: 424 Beacon St, Boston, MA, 02115, U.S.A.
Keywords: Sensitivity ; Specificity ; Verification bias ; Two-stage sampling ; Small sample size

The verification bias in a validation study for a new diagnostic procedure presents a statistical challenging. Adjusted estimates for sensitivity and specificity and their asymptotic variances have been developed previously. Simulation studies showed that the ROC curve constructed using the asymptotic variance estimation could produce misleading results when the number of verified non-diseased subjects is too small (n<5). In this paper, we propose a new approach to estimate the variability of adjusted sensitivity and specificity in attempt to address the small sample issue. This model allows a more complete count of the sampling errors. Our simulation results show considerable improvement in the coverage probability for sensitivity with the new confidence intervals when compared to the asymptotic ones especially in the case when the number of non-diseased subjects is small.

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