Please Note: This seminar will be held in-person.
Student seminar series
Assistant Professor, University of Waterloo
Location: M3 3127
Latent class models for an individual participant data meta-analyses of diagnostic test accuracy studies
The Patient Health Questionnaire-9 (PHQ-9) screening tool has been evaluated via individual participant data meta-analysis (IPDMA) against various reference standards, including semi-structured, fully structured, and the Mini International Neuropsychiatric (MINI) interviews that have been shown to have different accuracy for diagnosing major depression. This could influence estimates of PHQ-9 sensitivity and specificity and depression prevalence. Latent class models (LCMs) have been used to correct for imperfect reference or gold standards in an aggregate data meta-analysis (ADMA) of diagnostic test accuracy studies. Thus, we aimed to develop and validate LCMs that, in addition to depression prevalence, simultaneously estimate the accuracy of the PHQ-9 and different reference standards in the context of IPDMA. We will illustrate the models using a simulation study and a database that consists of more than 100 studies and 46,000 participants on the most commonly used tool for detecting major depression – the PHQ-9, and diagnostic interviews such as the Structured Clinical Interview for DSM (SCID), Composite International Diagnostic Interview (CIDI), and the Mini International Neuropsychiatric Interview (MINI). The new LCMs will be compared to the standard bivariate random-effects model (BREM) for IPDMA in terms of PHQ-9 accuracy estimates and their precision.