Osband's principle for identification functions.

Given a statistical functional of interest such as the mean or median, a (strict) identification function is zero in expectation at (and only at) the true functional value. Identification functions are key objects in forecast validation, statistical estimation and dynamic modelling. For a possibly vector-valued functional of interest, we fully characterise the class of (strict) identification functions subject to mild regularity conditions.

Probabilistic analysis of COVID-19 patients' individual length of stay in Swiss intensive care units.

RATIONALE: The COVID-19 pandemic induces considerable strain on intensive care unit resources. OBJECTIVES: We aim to provide early predictions of individual patients' intensive care unit length of stay, which might improve resource allocation and patient care during the on-going pandemic. METHODS: We developed a new semiparametric distributional index model depending on covariates which are available within 24h after intensive care unit admission. The model was trained on a large cohort of acute respiratory distress syndrome patients out of the Minimal Dataset of the Swiss Society of Intensive Care Medicine. Then, we predict individual length of stay of patients in the RISC-19-ICU registry. MEASUREMENTS: The RISC-19-ICU Investigators for Switzerland collected data of 557 critically ill patients with COVID-19. MAIN RESULTS: The model gives probabilistically and marginally calibrated predictions which are more informative than the empirical length of stay distribution of the training data. However, marginal calibration was worse after approximately 20 days in the whole cohort and in different subgroups. Long staying COVID-19 patients have shorter length of stay than regular acute respiratory distress syndrome patients. We found differences in LoS with respect to age categories and gender but not in regions of Switzerland with different stress of intensive care unit resources. CONCLUSION: A new probabilistic model permits calibrated and informative probabilistic prediction of LoS of individual patients with COVID-19. Long staying patients could be discovered early. The model may be the basis to simulate stochastic models for bed occupation in intensive care units under different casemix scenarios.

Stereological estimation of particle shape from vertical sections.

In the present paper, we describe a new simple stereological method of estimating volume tensors in 3D from vertical sections. The volume tensors provide information about particle shape in 3D. In a model-based setting, the method requires that the particle distribution is invariant under rotations around the vertical axis. In a design-based approach, where the vertical section is uniformly rotated around the vertical axis, the method provides information about an index of elongation of the particles in the direction of the vertical axis. The method has been implemented on human brain tissue for the analysis of neurons in layer III of the medial frontal gyrus of Brodmann Area 46. In the actual implementation, the new estimator shows similar precision as an earlier estimator, based on an optical rotator design, but it is a factor 3 faster to collect the measurements for the new estimator. Furthermore, the calculations needed for determining the new estimator are much simpler. LAY DESCRIPTION: A new method is described for estimating volume tensors in 3 dimensions based on the stereological method: planar rotator. In general, volume tensors may provide information about particle volume, shape and direction. The new estimator was implemented on human brain tissue for the analysis of neurons in layer III of the medial frontal gyrus on Brodmann Area 46 and compared to a previous published method, based on an optical rotator design. The new estimator shows similar precision as the earlier estimator, but it is a factor 3 faster to collect the measurements. Besides, the calculations behind the new estimator are simpler and easier to implement to a software program.