| Summary: | <p>Abstract</p> <p>Background</p> <p>As an increasingly large number of meta-analyses are published, quantitative methods are needed to help clinicians and systematic review teams determine when meta-analyses are not up to date.</p> <p>Methods</p> <p>We propose new methods for determining when non-significant meta-analytic results might be overturned, based on a prediction of the number of participants required in new studies. To guide decision making, we introduce the <it>"new participant ratio"</it>, the ratio of the actual number of participants in new studies to the predicted number required to obtain statistical significance. A simulation study was conducted to study the performance of our methods and a real meta-analysis provides further evidence.</p> <p>Results</p> <p>In our three simulation configurations, our diagnostic test for determining whether a meta-analysis is out of date had sensitivity of 55%, 62%, and 49% with corresponding specificity of 85%, 80%, and 90% respectively.</p> <p>Conclusions</p> <p>Simulations suggest that our methods are able to detect out-of-date meta-analyses. These quick and approximate methods show promise for use by systematic review teams to help decide whether to commit the considerable resources required to update a meta-analysis. Further investigation and evaluation of the methods is required before they can be recommended for general use.</p>
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