Search

We introduce a novel statistical significance-based approach for clustering hierarchical data using semi-parametric linear mixed-effects models designed for responses with laws in the exponential family (e.g., Poisson and Bernoulli). Within the family of semi-parametric mixed-effects models, a latent clustering structure of the highest-level units can be identified by assuming the random effects to follow a discrete distribution with an unknown number of support points. We achieve this by computing {\alpha}-level confidence regions of the estimated support point and identifying statistically different clusters. At each iteration of a tailored Expectation Maximization algorithm, the two closest estimated support points for which the confidence regions overlap collapse. Unlike the related state-of-the-art methods that rely on arbitrary thresholds to determine the merging of close discrete masses, the proposed approach relies on conventional statistical confidence levels, thereby avoiding the use of discretionary tuning parameters. To demonstrate the effectiveness of our approach, we apply it to data from the Programme for International Student Assessment (PISA - OECD) to cluster countries based on the rate of innumeracy levels in schools. Additionally, a simulation study and comparison with classical parametric and state-of-the-art models are provided and discussed.

This study offers original insights to further the ongoing debate about the stability of ‘hospital effect’ on performance with administrative data as a source of evidence. The overall assumption behind is investigating the debate on organizing the hospitals by learning from their previous performance to transform change. In this way, one available evidence is the administrative data that are collected in healthcare systems to document their activity and have had an administrative rational behind. Using statistical tests (multi-level hierarchical logistic models) on the variation of performance of the hospital, we used 78,907 records for Adult Heart Failure (HF) patients from 117 hospitals in Lombardy Region considering only Incident cases from 2010 to 2012 to answer to our research questions. what we observed was that mortality and readmission were explained by a few different hospital characteristics like the percentage of surgical DRG and type of hospital in mortality model and mean length of stay in readmission one. One explanation could be the case of connection between hospital managers’ capabilities to implement short-term improvement.

The theory of measurements continuous in time in quantum mechanics (quantum continual measurements) has been formulated by using the notions of instrument and positive operator valued measure, functional integrals, quantum stochastic differential equations and classical stochastic differential equations (SDE's). Various types of SDE's are involved, and precisely linear and non linear equations for vectors in Hilbert spaces and for trace-class operators. All such equations contain either a diffusive part, or a jump one, or both. In this paper we introduce a class of linear SDE's for trace-class operators, relevant to the theory of continual measurements, and we recall how such SDE's are related to instruments and master equations and, so, to the general formulation of quantum mechanics. We do not present the Hilbert space formulation of such SDE's and we make some mathematical simplifications: no time dependence is introduced into the coefficients and only bounded operators on the Hilbert space of the quantum system are considered. Then we introduce the notion of a posteriori state and the non linear SDE satisfied by such states and we give conditions from which such equation is assured to preserve pure states and to send any mixed state into a pure one for large times. Finally we review the known results about the existence and uniqueness of invariant measures in the purely diffusive case and we give some concrete examples of physical systems., Comment: 12 pages. Submitted to proceedings of "Stochastic partial differential equations (Levico Terme, Italy, January 2000)"

In pancreatic ductal adenocarcinomas (PDAC), lymphoid infiltrates, comprised mainly of Th2 cells, predict a poor survival outcome in patients. IL4 signaling has been suggested to stabilize the Th2 phenotype in this setting, but the cellular source of IL4 in PDAC is unclear. Here, we show that basophils expressing IL4 are enriched in tumor-draining lymph nodes (TDLN) of PDAC patients. Basophils present in TDLNs correlated significantly with the Th2/Th1 cell ratio in tumors, where they served as an independent prognostic biomarker of patient survival after surgery. Investigations in mouse models of pancreatic cancer confirmed a functional role for basophils during tumor progression. The recruitment of basophils into TDLN relied partly upon the release of chemokine CCL7/MCP3 by "alternatively activated" monocytes, whereas basophil activation was induced by T-cell–derived IL3. Our results show how basophils recruited and activated in TDLNs under the influence of the tumor microenvironment regulate tumor-promoting Th2 inflammation in PDAC, helping in illuminating a key element of the immune milieu of pancreatic cancer. Cancer Res; 76(7); 1792–803. ©2016 AACR.