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A hierarchical regression approach to meta‐analysis of diagnostic test accuracy evaluations
Author(s) -
Rutter Carolyn M.,
Gatsonis Constantine A.
Publication year - 2001
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.942
Subject(s) - computer science , covariate , markov chain monte carlo , meta analysis , statistics , regression analysis , markov chain , data mining , econometrics , machine learning , bayesian probability , artificial intelligence , mathematics , medicine
An important quality of meta‐analytic models for research synthesis is their ability to account for both within‐ and between‐study variability. Currently available meta‐analytic approaches for studies of diagnostic test accuracy work primarily within a fixed‐effects framework. In this paper we describe a hierarchical regression model for meta‐analysis of studies reporting estimates of test sensitivity and specificity. The model allows more between‐ and within‐study variability than fixed‐effect approaches, by allowing both test stringency and test accuracy to vary across studies. It is also possible to examine the effects of study specific covariates. Estimates are computed using Markov Chain Monte Carlo simulation with publicly available software (BUGS). This estimation method allows flexibility in the choice of summary statistics. We demonstrate the advantages of this modelling approach using a recently published meta‐analysis comparing three tests used to detect nodal metastasis of cervical cancer. Copyright © 2001 John Wiley & Sons, Ltd.