Assessment of school performance through a multilevel latent Markov Rasch model

An extension of the latent Markov Rasch model is described for the analysis of binary longitudinal data with covariates when subjects are collected in clusters, e.g. students clustered in classes. For each subject, the latent process is used to represent the characteristic of interest (e.g. ability) conditional on the effect of the cluster to which he/she belongs. The latter effect is modeled by a discrete latent variable associated with each cluster. For the maximum likelihood estimation of the model parameters we outline an EM algorithm. We show how the proposed model may be used for assessing the development of cognitive Math achievement. This approach is applied to the analysis of a dataset collected in the Lombardy Region (Italy) and based on test scores over three years of middle-school students attending public and private schools

Social Interaction in Patients'�Hospital Choice: Evidences from Italy

In this paper we study the influence of social interaction on patients' hospital choice and its relationship with quality delivered by hospitals, using Italian data. We explore the impact on individual choices of a set of variables such as travel distance, individual- and hospital-specific characteristics, as well as a variable capturing the effect of the neighbourhood. The richness of our data allows us to disentangle contextual effects from the influence of information sharing on patients' hospital choices. We then use this framework to assess how such interaction is related to clinical hospital quality. Results show that network effect plays an important role in hospital choices, although it is less relevant for larger hospitals. Another empirical finding is the existence of a negative relationship between the degree of interaction among individuals and the quality delivered by hospitals. The absence of a source of information on the quality of hospitals accessible to all individuals, such as guidelines or star ratings, exacerbates the importance of information gathered locally in hospital choices, which may result in a lower degree of competition among hospitals and lower quality.health care, social interaction, quality

Local Statistical Modeling via Cluster-Weighted Approach with Elliptical Distributions

Cluster Weighted Modeling (CWM) is a mixture approach regarding the modelisation of the joint probability of data coming from a heterogeneous population. Under Gaussian assumptions, we investigate statistical properties of CWM from both the theoretical and numerical point of view; in particular, we show that CWM includes as special cases mixtures of distributions and mixtures of regressions. Further, we introduce CWM based on Student-t distributions providing more robust fitting for groups of observations with longer than normal tails or atypical observations. Theoretical results are illustrated using some empirical studies, considering both real and simulated data.Cluster-Weighted Modeling, Mixture Models, Model-Based Clustering

On the Relationships among Latent Variables and Residuals in PLS Path Modeling: the Formative-Reflective Scheme

A new approach for the estimation and the validation of a Structural Equation Model with a formative-reflective scheme is presented. The basis of the paper is a proposal for overcoming a potential deficiency of PLS Path Modeling. In the PLS approach the reflective scheme assumed for the endogenous latent variables is inverted; moreover, the model errors are not explicitly taken into account for the estimation of the endogenous latent variables. The proposed approach utilizes all the relevant information in the formative manifest variables providing solutions which respect the causal structure of the model. The estimation procedure is based on the optimization of the redundancy criterion. The new approach, entitled Redundancy Analysis approach to Path Modeling is compared with both traditional PLS Path Modeling and LISREL methodology, on the basis of real and simulated data.Latent Variables, Partial Least Squares, PLS Path Modeling, Redundancy Analysis, LISREL Model

HUMAN CAPITAL MEASUREMENT: A SURVEY

Abstract After a short history of the concept of human capital (henceforth HC) in economic thought (Section 1), this study presents the two main methods for estimating the value of the stock of HC – the retrospective and prospective one – with a review of the models proposed (Section 2). These methods are linked both to the theory of HC investment as a rational choice (Section 3), the literature analysing the contribution of HC investment to economic growth and the HC estimating method through educational attainment (Section 4). The more recent literature on HC as a latent variable is also assessed (Section 5) and a new method of estimation where HC is seen both as an unknown function of formative indicators and as a 'latent effect' underlying earned income is proposed (Section 6). Section 7 concludes

El deseo derrotará las nuevas esclavitudes

En este texto, el autor abordará la situación actual en relación a tendencias mundiales dentro de la economía, tecnología y relaciones sociales. Así, frente a cada época tecnológica, se afirmará que el deseo empujará al hombre a buscar el pan y el agua, a interesarse por sus problemas y los problemas de los otros, a mejorar sus condiciones y la de sus hermanos hombres. Cabe agregar que este texto se trata de una traducción del italiano

Bayesian Using Gibbs Sampling Manual. Cambridge: MRC Bio-statistic Unit

In this paper we propose a methodology for measuring the 'relative effectiveness' of healthcare services (i.e. the effect of hospital care on patients) under general conditions, in which: α) a healthcare outcome underlies qualitative and quantitative observable indicators; β) we are interested in studying the simultaneous dependency of multiple outcomes on covariates (where the outcomes can also be correlated to each other); γ) the relative effectiveness is adjusted for hospital-specific covariates; δ) we hypothesise a general distribution for random disturbances and the random parameters of relative effectiveness. For this topic, a generalisation of the SURE (seemingly unrelated regression equations) multilevel model is proposed. Albert & Chib (1997, J. Am. Stat. Assoc., 92, 916-925). In addition, a new theoretical result regarding the joint posterior distribution for the parameters is provided. The model proposed has been implemented for an effectiveness study of a selection of Lombard hospitals

EPMA position paper in cancer:current overview and future perspectives

At present, a radical shift in cancer treatment is occurring in terms of predictive, preventive, and personalized medicine (PPPM). Individual patients will participate in more aspects of their healthcare. During the development of PPPM, many rapid, specific, and sensitive new methods for earlier detection of cancer will result in more efficient management of the patient and hence a better quality of life. Coordination of the various activities among different healthcare professionals in primary, secondary, and tertiary care requires well-defined competencies, implementation of training and educational programs, sharing of data, and harmonized guidelines. In this position paper, the current knowledge to understand cancer predisposition and risk factors, the cellular biology of cancer, predictive markers and treatment outcome, the improvement in technologies in screening and diagnosis, and provision of better drug development solutions are discussed in the context of a better implementation of personalized medicine. Recognition of the major risk factors for cancer initiation is the key for preventive strategies (EPMA J. 4(1):6, 2013). Of interest, cancer predisposing syndromes in particular the monogenic subtypes that lead to cancer progression are well defined and one should focus on implementation strategies to identify individuals at risk to allow preventive measures and early screening/diagnosis. Implementation of such measures is disturbed by improper use of the data, with breach of data protection as one of the risks to be heavily controlled. Population screening requires in depth cost-benefit analysis to justify healthcare costs, and the parameters screened should provide information that allow an actionable and deliverable solution, for better healthcare provision

Social Interaction in Patients'�Hospital Choice: Evidences from Italy

In this paper we study the influence of social interaction on patients' hospital choice and its relationship with quality delivered by hospitals, using Italian data. We explore the impact on individual choices of a set of variables such as travel distance, individual- and hospital-specific characteristics, as well as a variable capturing the effect of the neighbourhood. The richness of our data allows us to disentangle contextual effects from the influence of information sharing on patients' hospital choices. We then use this framework to assess how such interaction is related to clinical hospital quality. Results show that network effect plays an important role in hospital choices, although it is less relevant for larger hospitals. Another empirical finding is the existence of a negative relationship between the degree of interaction among individuals and the quality delivered by hospitals. The absence of a source of information on the quality of hospitals accessible to all individuals, such as guidelines or star ratings, exacerbates the importance of information gathered locally in hospital choices, which may result in a lower degree of competition among hospitals and lower quality