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A comparative meta-analysis on the variability in test performance among FDA-licensed enzyme immunosorbent assays for
1Department of Biostatistics, Harvard School of Public Health, , Boston, MA 02115, USA.
Background:
Although independently published studies have compared diagnostic test performance among various manufactured enzyme immunosorbent assays (EIAs) used in HIV antibody testing, none have attempted to formally synthesize such results through a comparative meta-analysis. In particular, no estimates of post-FDA approval test performance, in terms of sensitivity and specificity, and their associated variability within each manufacturer, has been reported in the literature, along with an analysis of the relative differences in manufacturer test performance in practice (after FDA approval).
Methods:
Retrieval of studies was done using several searching strategies, while retrieval of manufacturer information was done through package inserts and direct contacts. Comparisons of HIV antibody test performance across manufacturers and within a single manufacturer were made based on 16 estimates (from 11 articles) and 33 estimates (from 19 articles), respectively. A generalized linear model, based upon Bayes estimates of sensitivity and specificity, was used to assess the impact of several study-level covariates on the performance of these EIAs, with overall estimates of manufacturer test performance and associated variability obtained based on generalized estimating equations.
Results:
Estimates of test performance were obtained across studies, with a significant (P < 0.01) difference between manufacturers. The test performance of each manufacturer significantly interacted (P < 0.05) with the following study-level covariates: type of population screened, year of diagnostic testing and study quality. Among a single manufacturer, Abbott, significant improvement in estimates of test sensitivity (P < 0.01) and specificity (P < 0.01) was observed with each newly produced antibody kit.
Conclusion:
Estimates on the relative differences in test performance within each manufacturer may be used for guiding decisions on the choice of EIA test kit for HIV antibodies, given the type of population screened, as well as cost and time considerations. In addition, the results of this meta-analysis may be used in modeling HIV prevalence when used as prior information within a Bayesian framework or for standardizing test results among various manufacturers.