A0830
Title: Diagnostic test accuracy meta-analysis based on exact within-study variance estimation method
Authors: Olana Dabi - University of Waterloo (Canada)
Zelalem Negeri - University of Waterloo (Canada) [presenting]
Abstract: Meta-analysis of diagnostic test accuracy studies commonly synthesizes study-specific test sensitivity and test specificity from different studies that aim to quantify the screening or diagnostic performance of a common index test of interest. A bivariate random effects model that utilizes the logit transformation of sensitivity and specificity and accounts for the within- and between-study heterogeneity is commonly used to make statistical inferences about the unknown test characteristics. However, it is well reported that this model may lead to misleading inference since it employs the logit transformation and approximate within-study variance estimate. Alternative transformations which do not require continuity corrections, such as the arcsine square root and Freeman-Tukey double arcsine, were recently proposed to overcome the former limitation. However, these solutions also suffer from using approximate within-study variance estimates, which can only be justified when within-study sample sizes are large. To overcome these problems, an exact within-study variance estimation approach is proposed, which does not require a continuity correction and is invariant to transformations. The new method and the existing approaches are evaluated using real-life and simulated meta-analytic data.
