Latent class models have been commonly applied to correct for imperfect reference or gold standards in diagnostic test accuracy studies. Most of these latent class models use the Bayesian analysis approach to estimate the unknown model parameters. To the best of our knowledge, there are no methodological studies that attempted to account for imperfect reference standards in the context of individual participant data meta-analyses (IPDMA) of diagnostic test accuracy studies. Therefore, the objective of this study is to develop and validate latent class models for IPDMA to estimate the diagnostic test accuracy of both an index test and imperfect reference standards by exploring both Frequentist and Bayesian approaches to the problem. We will illustrate the models using our database that consists of more than 100 studies and 46,000 participants on the most commonly used tool for detecting major depression – the Patient Health Questionnaire-9 (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).