This is the third in a series of four articles

Tests are routinely used in medicine to screen for, diagnose, grade, and monitor the progression of disease. Diagnostic information is obtained from a multitude of sources, including imaging and biochemical technologies, pathological and psychological investigations, and signs and symptoms elicited during history taking and clinical examinations.1 Each of these items of information can be regarded as a result of a separate diagnostic or screening “test.” Systematic reviews of evaluations of tests are undertaken for the same reasons as systematic reviews of treatment interventions: to produce estimates of test performance and impact based on all available evidence, to evaluate the quality of published studies, and to account for variation in findings between studies.2^–5 Reviews of studies of diagnostic accuracy involve the same key stages of defining questions, searching the literature, evaluating studies for eligibility and quality, and extracting and synthesising data. However, studies that evaluate the accuracy of tests have a unique design requiring different criteria to appropriately assess the quality of studies and the potential for bias. Additionally, each study reports a pair of related summary statistics (for example, sensitivity and specificity) rather than a single statistic (such as a risk ratio) and hence requires different statistical methods to pool the results of the studies. This article concentrates on the dimensions of study quality and the advantages and disadvantages of different summary statistics for combining studies in meta-analysis. Other aspects, including searching the literature and further technical details, are discussed elsewhere.6