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.

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