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Currently, most studies that employ dynamic causal modeling (DCM) use random-effects (RFX) analysis to make group inferences, applying a second-level frequentist test to subjects' parameter estimates. In some instances, however, fixed-effects (FFX) analysis can be more appropriate. Such analyses can be implemented by combining the subjects' posterior densities according to Bayes' theorem either on a multivariate (Bayesian parameter averaging or BPA) or univariate basis (posterior variance weighted averaging or PVWA), or by applying DCM to time-series averaged across subjects beforehand (temporal averaging or TA). While all these FFX approaches have the advantage of allowing for Bayesian inferences on parameters a systematic comparison of their statistical properties has been lacking so far.Based on simulated data generated from a two-region network we examined the effects of signalto-noise ratio (SNR) and population heterogeneity on group-level parameter estimates. Data sets were simulated assuming either a homogeneous large population (N=60) with constant connectivities across subjects or a heterogeneous population with varying parameters. TA showed advantages at lower SNR but is limited in its applicability. Because BPA and PVWA take into account posterior (co)variance structure, they can yield non-intuitive results when only considering posterior means. This problem is relevant for high SNR data, pronounced parameter interdependencies and when FFX assumptions are violated (i.e. inhomogeneous groups). It diminishes with decreasing SNR and is absent for models with independent parameters or when FFX assumptions are appropriate. Group results obtained with these FFX approaches should therefore be interpreted carefully by considering estimates of dependencies among model parameters.

Background The transfer of classic concepts of competency-based medical education into clinical practice has been proven to be difficult in the past, being described as partially fragmented, misleading and inadequate. At the beginning of training, novice doctors commonly feel overwhelmed, overloaded and exposed to extreme time pressure. The discrepancy between expected and actual clinical competence of doctors at the start of their speciality training jeopardizes patient safety. The framework of Entrustable Professional Activities (EPAs) is a promising instrument to effectively integrate competency-based training into clinical practice and may help to close this gap and consequently to improve patient safety. Methods For anaesthesiology, we developed 5 EPAs for final-year medical students. The EPAs comprised the following seven categories: 1. Title, 2. Specifications, 3. Limitations, 4. Competency domains, 5. Knowledge, abilities and skills, professional attitudes, 6. Assessment and 7. Entrustment. Based on a modified, online-based Delphi study, we further developed and refined these EPAs. Education experts were recruited from the alumni network of the Master of Medical Education (MME) degree course from the University of Heidelberg, Germany. Results 28 data sets were evaluated in three Delphi rounds. 82% of study participants had previous experience with EPAs. Qualitative and quantitative data formed the basis during the iterative process and resulted in complete descriptions of 5 EPAs for final-year medical students in anaesthesiology. Conclusions Our study including the associated description of 5 EPAs represent a further step and starting point for EPA-based curricula in medical training in Germany linking undergraduate training, to residency training and continuous medical education.

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