Background: A meta-analysis of diagnostic test studies provides evidencebased results regarding the accuracy of a particular test and usually involves synthesising aggregate data (AD) from each study, such as the two by two tables of diagnostic accuracy. A bivariate random-effects metaanalysis (BRMA) can appropriately synthesise these tables and leads to clinical results such as the mean sensitivity and mean specificity across studies. However, translating such results into practice may be limited by between-study heterogeneity and the fact that they relate to some ’average’ patient across studies. Objectives: In this talk, we will describe how the meta-analysis of individual patient data (IPD) from diagnostic studies can lead to more clinically meaningful results tailored to the individual patient. Methods: We describe IPD models that extend the BRMA framework to include study-level covariates, which help explain the between-study heterogeneity, and also patient-level covariates, which allow the interaction between test accuracy and patient characteristics to be assessed. Results and Conclusions: We will show how the inclusion of patient-level covariates requires careful separation of within-study and across-study accuracy-covariate interactions, as the latter are particularly prone to confounding. Our models are assessed through simulation and are extended to allow IPD studies to be combined with AD studies, as IPD are not always available for all studies. Application is made to 23 studies assessing the accuracy of ear temperature for diagnosing fever in children, with 16 IPD and 7 AD studies. The models reveal that between-study heterogeneity is partly explained by the use of different measurement devices and that there is no evidence that individual age modifies diagnostic accuracy.