Multivariate random effects meta-analysis of diagnostic tests with multiple thresholds

Background. Bivariate random effects meta-analysis of diagnostic tests is becoming a well established approach when studies present one two-by-two table or one pair of sensitivity and specificity. When studies present multiple thresholds for test positivity, usually meta-analysts reduce the data to a two-by-two table or take one threshold value at a time and apply the well developed meta-analytic approaches. However, this approach does not fully exploi

The Alcohol Use Disorders Identification Test (AUDIT) as a screening tool for excessive drinking in primary care: reliability and validity of a French version.

BACKGROUND: Excessive drinking is a major problem in Western countries. AUDIT (Alcohol Use Disorders Identification Test) is a 10-item questionnaire developed as a transcultural screening tool to detect excessive alcohol consumption and dependence in primary health care settings. OBJECTIVES: The aim of the study is to validate a French version of the Alcohol Use Disorders Identification Test (AUDIT). METHODS: We conducted a validation cross-sectional study in three French-speaking areas (Paris, Geneva and Lausanne). We examined psychometric properties of AUDIT as its internal consistency, and its capacity to correctly diagnose alcohol abuse or dependence as defined by DSM-IV and to detect hazardous drinking (defined as alcohol intake >30 g pure ethanol per day for men and >20 g of pure ethanol per day for women). We calculated sensitivity, specificity, positive and negative predictive values and Receiver Operator Characteristic curves. Finally, we compared the ability of AUDIT to accurately detect "alcohol abuse/dependence" with that of CAGE and MAST. RESULTS: 1207 patients presenting to outpatient clinics (Switzerland, n = 580) or general practitioners' (France, n = 627) successively completed CAGE, MAST and AUDIT self-administered questionnaires, and were independently interviewed by a trained addiction specialist. AUDIT showed a good capacity to discriminate dependent patients (with AUDIT > or =13 for males, sensitivity 70.1%, specificity 95.2%, PPV 85.7%, NPV 94.7% and for females sensitivity 94.7%, specificity 98.2%, PPV 100%, NPV 99.8%); and hazardous drinkers (with AUDIT > or =7, for males sensitivity 83.5%, specificity 79.9%, PPV 55.0%, NPV 82.7% and with AUDIT > or =6 for females, sensitivity 81.2%, specificity 93.7%, PPV 64.0%, NPV 72.0%). AUDIT gives better results than MAST and CAGE for detecting "Alcohol abuse/dependence" as showed on the comparative ROC curves. CONCLUSIONS: The AUDIT questionnaire remains a good screening instrument for French-speaking primary care

Likelihood-Based Clustering of Meta-Analytic SROC Curves

Meta-analysis of diagnostic studies experience the common problem that different studies mightnot be comparable since they have been using a different cut-off value for the continuous or orderedcategorical diagnostic test value defining different regions for which the diagnostic test is defined to bepositive. Hence specificities and sensitivities arising from different studies might vary just because theunderlying cut-off value had been different. To cope with the cut-off value problem interest is usuallydirected towards the receiver operating characteristic (ROC) curve which consists of pairs of sensitivitiesand false-positive rates (1-specificity). In the context of meta-analysis one pair represents one study andthe associated diagram is called an SROC curve where the S stands for “summary”. In meta-analysis ofdiagnostic studies emphasis has traditionally been placed on modelling this SROC curve with the intentionof providing a summary measure of the diagnostic accuracy by means of an estimate of the summary ROCcurve. Here, we focus instead on finding sub-groups or components in the data representing differentdiagnostic accuracies. The paper will consider modelling SROC curves with the Lehmann family whichis characterised by one parameter only. Each single study can be represented by a specific value of thatparameter. Hence we focus on the distribution of these parameter estimates and suggest modelling apotential heterogeneous or cluster structure by a mixture of specifically parameterised normal densities.We point out that this mixture is completely nonparametric and the associated mixture likelihood is welldefinedand globally bounded. We use the theory and algorithms of nonparametric mixture likelihoodestimation to identify a potential cluster structure in the diagnostic accuracies of the collection of studiesto be analysed. Several meta-analytic applications on diagnostic studies, including AUDIT and AUDIT-Cfor detection of unhealthy alcohol use, the mini-mental state examination for cognitive disorders, as wellas diagnostic accuracy inspection data on metal fatigue of aircraft spare parts, are discussed to illustratethe methodology