Statistical properties of two-color randomly reinforced urn design targeting fixed allocations

This paper deals with the statistical properties of a response adaptive design, described in terms of a two colors urn model, targeting prespecified asymptotic allocations. Results on the rate of divergence of number of patients assigned to each treatment are proved as well as on the asymptotic behavior of the urn composition. Suitable statistics are introduced and studied to test the hypothesis on treatments’ difference

Non-parametric frailty Cox models for hierarchical time-to-event data.

We propose a novel model for hierarchical time-to-event data, for example, healthcare data in which patients are grouped by their healthcare provider. The most common model for this kind of data is the Cox proportional hazard model, with frailties that are common to patients in the same group and given a parametric distribution. We relax the parametric frailty assumption in this class of models by using a non-parametric discrete distribution. This improves the flexibility of the model by allowing very general frailty distributions and enables the data to be clustered into groups of healthcare providers with a similar frailty. A tailored Expectation-Maximization algorithm is proposed for estimating the model parameters, methods of model selection are compared, and the code is assessed in simulation studies. This model is particularly useful for administrative data in which there are a limited number of covariates available to explain the heterogeneity associated with the risk of the event. We apply the model to a clinical administrative database recording times to hospital readmission, and related covariates, for patients previously admitted once to hospital for heart failure, and we explore latent clustering structures among healthcare providers.MR

Semiparametric mixed-effects models for unsupervised classification of Italian schools

The main purpose of the paper is to improve research on school effectiveness by applying a new strategy for uncovering subpopulations of schools that differ in terms of distributionof student outcomes. We propose a semiparametric mixed effects model with an expectation–maximization algorithm to estimate its parameters and we apply it to the Italian Institute for theEducational Evaluation of Instruction and Training data of 2013–2014 as a tool for the identification of latent subpopulations of schools. The semiparametric assumption provides the random effects of the mixed effects model to be distributed according to a discrete distribution with an(a priori) unknown number of support points. This modelling induces an automatic clustering of schools (the higher level of hierarchy), where schools within the same cluster share the same random effects. The latent subpopulations of schools identified may then be exploited through the use of multinomial models that include school level features. The novelties introduced by this paper are twofold: first, the semiparametric expectation–maximization algorithm is an innovative method that could be used in many classification problems; second, its application to education data represents a new approach to study school effectiveness

Optical discrimination between malignant and benign breast lesions

Time domain multi-wavelength (635 to 1060 nm) optical mammography was performed on 82 subjects with breast lesions (45 malignant and 38 benign lesions). A perturbative approach based on the high-order calculation of the pathlength of photons inside the lesion was applied to estimate differences between lesion and average healthy breast tissue in terms of: i) absorption properties, and ii) concentration of the major tissue constituents (oxy- and deoxy-hemoglobin, water, lipid and collagen). The absorption difference a between lesion and healthy tissue is significantly different for malignant vs. benign lesions at all wavelengths. Logistic regression fitted to the absorption data identifies 975 nm as the key wavelength to discriminate malignant from benign lesions. When the difference in tissue composition between lesion and healthy tissue is considered, malignant lesions are characterized by significantly higher collagen content than benign lesions. Also the best model for the discrimination of malignant lesions obtained applying regression logistic to tissue composition is based only on collagen

Non-invasive optical estimate of tissue composition to differentiate malignant from benign breast lesions: A pilot study

Several techniques are being investigated as a complement to screening mammography, to reduce its false-positive rate, but results are still insufficient to draw conclusions. This initial study explores time domain diffuse optical imaging as an adjunct method to classify non-invasively malignant vs benign breast lesions. We estimated differences in tissue composition (oxy-and deoxyhemoglobin, lipid, water, collagen) and absorption properties between lesion and average healthy tissue in the same breast applying a perturbative approach to optical images collected at 7 red-near infrared wavelengths (635-1060 nm) from subjects bearing breast lesions. The Discrete AdaBoost procedure, a machine-learning algorithm, was then exploited to classify lesions based on optically derived information (either tissue composition or absorption) and risk factors obtained from patient's anamnesis (age, body mass index, familiarity, parity, use of oral contraceptives, and use of Tamoxifen). Collagen content, in particular, turned out to be the most important parameter for discrimination. Based on the initial results of this study the proposed method deserves further investigation

Time domain diffuse optical spectroscopy: In vivo quantification of collagen in breast tissue

Time-resolved diffuse optical spectroscopy provides non-invasively the optical characterization of highly diffusive media, such as biological tissues. Light pulses are injected into the tissue and the effects of light propagation on re-emitted pulses are interpreted with the diffusion theory to assess simultaneously tissue absorption and reduced scattering coefficients. Performing spectral measurements, information on tissue composition and structure is derived applying the Beer law to the measured absorption and an empiric approximation to Mie theory to the reduced scattering. The absorption properties of collagen powder were preliminarily measured in the range of 600-1100 nm using a laboratory set-up for broadband time-resolved diffuse optical spectroscopy. Optical projection images were subsequently acquired in compressed breast geometry on 218 subjects, either healthy or bearing breast lesions, using a portable instrument for optical mammography that operates at 7 wavelengths selected in the range 635-1060 nm. For all subjects, tissue composition was estimated in terms of oxy- and deoxy-hemoglobin, water, lipids, and collagen. Information on tissue microscopic structure was also derived. Good correlation was obtained between mammographic breast density (a strong risk factor for breast cancer) and an optical index based on collagen content and scattering power (that accounts mostly for tissue collagen). Logistic regression applied to all optically derived parameters showed that subjects at high risk for developing breast cancer for their high breast density can effectively be identified based on collagen content and scattering parameters. Tissue composition assessed in breast lesions with a perturbative approach indicated that collagen and hemoglobin content are significantly higher in malignant lesions than in benign ones

Characterization of engineering student profiles at european institutions by using speet it-tool

The international ERASMUS+ project SPEET (Student Profile for Enhancing Engineering Tutoring) aims at opening a new perspective to university tutoring systems. Before looking for its nature, it’s recommended to have a look on the current use of data in education and on the concept of academic analytics basically defined as the process of evaluating and analysing data received from university systems for reporting and decision making reasons. The provided tools are freely available to anyone that has academic data to explore. The paper will present the architecture that is behind the presented IT tool, input data needed to operate and main functionalities as well as examples of use to show how academic data can be interpreted.info:eu-repo/semantics/publishedVersio

Evaluating the effect of healthcare providers on the clinical path of heart failure patients through a semi-Markov, multi-state model

Abstract: Background: Investigating similarities and differences among healthcare providers, on the basis of patient healthcare experience, is of interest for policy making. Availability of high quality, routine health databases allows a more detailed analysis of performance across multiple outcomes, but requires appropriate statistical methodology. Methods: Motivated by analysis of a clinical administrative database of 42,871 Heart Failure patients, we develop a semi-Markov, illness-death, multi-state model of repeated admissions to hospital, subsequent discharge and death. Transition times between these health states each have a flexible baseline hazard, with proportional hazards for patient characteristics (case-mix adjustment) and a discrete distribution for frailty terms representing clusters of providers. Models were estimated using an Expectation-Maximization algorithm and the number of clusters was based on the Bayesian Information Criterion. Results: We are able to identify clusters of providers for each transition, via the inclusion of a nonparametric discrete frailty. Specifically, we detect 5 latent populations (clusters of providers) for the discharge transition, 3 for the in-hospital to death transition and 4 for the readmission transition. Out of hospital death rates are similar across all providers in this dataset. Adjusting for case-mix, we could detect those providers that show extreme behaviour patterns across different transitions (readmission, discharge and death). Conclusions: The proposed statistical method incorporates both multiple time-to-event outcomes and identification of clusters of providers with extreme behaviour simultaneously. In this way, the whole patient pathway can be considered, which should help healthcare managers to make a more comprehensive assessment of performance