title: Using meta-analysis to inform the design of subsequent studies of diagnostic test accuracy.
abstract: An individual diagnostic accuracy study rarely provides enough information to make conclusive recommendations about the accuracy of a diagnostic test; particularly when the study is small. Meta-analysis methods provide a way of combining information from multiple studies, reducing uncertainty in the result and hopefully providing substantial evidence to underpin reliable clinical decision-making. Very few investigators consider any sample size calculations when designing a new diagnostic accuracy study. However, it is important to consider the number of subjects in a new study in order to achieve a precise measure of accuracy. Sutton et al. have suggested previously that when designing a new therapeutic trial, it could be more beneficial to consider the power of the updated meta-analysis including the new trial rather than of the new trial itself. The methodology involves simulating new studies for a range of sample sizes and estimating the power of the updated meta-analysis with each new study added. Plotting the power values against the range of sample sizes allows the clinician to make an informed decision about the sample size of a new trial. This paper extends this approach from the trial setting and applies it to diagnostic accuracy studies. Several meta-analytic models are considered including bivariate random effects meta-analysis that models the correlation between sensitivity and specificity. Copyright © 2012 John Wiley & Sons, Ltd.
authors.affiliation: Biostatistics Group, Department of Health Sciences, University of Leicester, Leicester, UK.
authors.forename: Sally R
authors.initials: SR
authors.lastname: Hinchliffe
authors.affiliation: Biostatistics Group, Department of Health Sciences, University of Leicester, Leicester, UK.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Crowther
authors.affiliation: Regional Department of Paediatric Haematology/Oncology, St James's Hospital, Leeds, UK.
authors.forename: Robert S
authors.initials: RS
authors.lastname: Phillips
authors.affiliation: Biostatistics Group, Department of Health Sciences, University of Leicester, Leicester, UK.
authors.forename: Alex J
authors.initials: AJ
authors.lastname: Sutton
keywords: diagnostic test; evidence based medicine; meta-analysis; sample size