Item selection by Latent Class-based methods

The evaluation of nursing homes is usually based on the administration of questionnaires made of a large number of polytomous items. In such a context, the Latent Class (LC) model represents a useful tool for clustering subjects in homogenous groups corresponding to different degrees of impairment of the health conditions. It is known that the performance of model-based clustering and the accuracy of the choice of the number of latent classes may be affected by the presence of irrelevant or noise variables. In this paper, we show the application of an item selection algorithm to real data collected within a project, named ULISSE, on the quality-of-life of elderly patients hosted in italian nursing homes. This algorithm, which is closely related to that proposed by Dean and Raftery in 2010, is aimed at finding the subset of items which provides the best clustering according to the Bayesian Information Criterion. At the same time, it allows us to select the optimal number of latent classes. Given the complexity of the ULISSE study, we perform a validation of the results by means of a sensitivity analysis to different specifications of the initial subset of items and of a resampling procedure

A bivariate finite mixture growth model with selection

AbstractA model is proposed to analyze longitudinal data where two response variables are available, one of which is a binary indicator of selection and the other is continuous and observed only if the first is equal to 1. The model also accounts for individual covariates and may be considered as a bivariate finite mixture growth model as it is based on three submodels: (i) a probit model for the selection variable; (ii) a linear model for the continuous variable; and (iii) a multinomial logit model for the class membership. To suitably address endogeneity, the first two components rely on correlated errors as in a standard selection model. The proposed approach is applied to the analysis of the dynamics of household portfolio choices based on an unbalanced panel dataset of Italian households over the 1998–2014 period. For this dataset, we identify three latent classes of households with specific investment behaviors and we assess the effect of individual characteristics on households' portfolio choices. Our empirical findings also confirm the need to jointly model risky asset market participation and the conditional portfolio share to properly analyze investment behaviors over the life-cycle

Oriented mentalization-based treatment for borderline personality disorder patients: Preliminary results at camposampiero mental health center

This contribution presents two brief reports about preliminary results of 18 months of oriented Mentalization-Based Treatment (MBT), with Borderline Personality Disorder patients, recruited at the Camposampiero Mental Health Center. Following in large part Bateman and Fonagy guidelines for MBT in institutional settings, this paper presents preliminary results in two brief reports related to two cohorts of patient underwent to the oriented MBT in Camposampiero Mental Health Center (MHC). In the first study, we analyzed a group of 9 patients: an anamnestic schedule was administered; then, symptoms (SCL-90-R), psychodiagnostic scale and global health functioning (Health of the Nation Outcome Scale, Structured Clinical Interview for DSM-IV Axis II Disorder [SCID-II], Global Assessment of Functioning, [GAF]), data on service impact and service costs (Cassel Community Adjustment Questionnaire, Patient Evaluation Schedule and folder data) were provided at the beginning (T0), at the end of the treatment (T2) and 1 year after the end of the MBT project. The second study showed a micro-analytical change on a second patient cohort (n=6) at T0, 3, 6 and 9 months (T1) were presented considering specifically mentalization (Comparative Psychotherapy Process Scale, Modes of Mentalization Scale, Mentalization Imbalances Scale) and patienttherapist session evaluation trends (Session Evaluation Questionnaire) and patient reflective functioning at T0 and T2 (Reflective Functioning Questionnaire). Aims and hypotheses of the two studies pointed out how the oriented MBT bring to an improvement of the overall functioning of the patients, a reduction of the symptoms, a decrease of the diagnostic criteria for the Borderline Personality Disorder (BPD) and the other Axis II disorders, a reduction of the workload hours for the MHC staff and of the costs of the assistance. The analyses of both the studies were carried out using non-parametric statistics (Friedman test, Spearman correlation, Chi-square). Preliminary results confirmed the improvement in the overall functioning of patients (GAF), the reduction in BPD-related symptoms and in diagnostic criteria for BPD (SCID-II), the improvement of patients’ mentalization skills, and a significant reduction in workload for health staff. Standing the limits and the preliminary results, the two brief reports demonstrate the feasibility of an oriented MBT within an Italian Public Service and the effectiveness of this treatment pathway for patients with BPD, leaving some open questions to stimulate a fruitful clinical discussion

LMest: An R Package for Latent Markov Models for Longitudinal Categorical Data

Latent Markov (LM) models represent an important class of models for the analysis of longitudinal data, especially when response variables are categorical. These models have a great potential of application in many fields, such as economics and medicine. We illustrate the R package LMest that is tailored to deal with the basic LM model and some extended formulations accounting for individual covariates and for the presence of unobserved clusters of units having the same initial and transition probabilities (mixed LM model). The main functions of the package are tailored to parameter estimation through the expectation-maximization algorithm, which is based on suitable forwardbackward recursions. The package also permits local and global decoding and to obtain standard errors for the parameter estimates. We illustrate the use of the package and its main features through some empirical examples in the fields of labour market, health, and criminology

Composite likelihood inference for hidden Markov models for dynamic networks

We introduce a hidden Markov model for dynamic network data where directed relations among a set of units are observed at different time occasions. The model can also be used with minor adjustments to deal with undirected networks. In the directional case, dyads referred to each pair of units are explicitly modelled conditional on the latent states of both units. Given the complexity of the model, we propose a composite likelihood method for making inference on its parameters. This method is studied in detail for the directional case by a simulation study in which different scenarios are considered. The proposed approach is illustrated by an example based on the well-known Enron dataset about email exchange