Meta-Analysis of Binary Data Based upon Dichotomized Criteria

Abstract

Abstract. This paper considers meta-analysis of binary data that use a dichotomized continuous score. Classification into two categories, e.g., qualified or not qualified, is often based upon a threshold or cut-off value. This threshold might vary between studies since intentionally different values are used. However, conventional meta-analysis methodology analyzing sensitivity and specificity separately might then be confounded by a potentially unknown variation of the cut-off value. In order to cope with varying thresholds, an overall estimate of the misclassification error is suggested instead, which is equivalent to the well-known Youden index. It is argued that this index is less prone to between-study variation of cut-off values. To adjust for potential study effects a Mantel-Haenszel estimator of the overall misclassification error is suggested. Arguments are illustrated using, as an example, the diagnosis of alcoholism using the Alcohol Use Disorders Identification Test (AUDIT).