Dropping out of University of Salerno: a survival approach

The aim of this paper is to analyse and model the interval of time between the first enrolment at University and the first occurrence of non-enrolment, so that the event of interest is the dropping out of University. The interest is focused on computing the probability of surviving at the University and analysing which personal, familiar and social characteristics may influence the non-completion of students’ academic carrier, using Survival Analysis techniques. The dataset analysed is collected from the Central Administrative Office of the University of Salerno, and includes all full-time students enrolled at the 2002-2003 academic year, followed for five years, until the 2006-2007 academic year. Those students can either complete their study and receive their degree or leave the university, that is the event of our interest. The estimation of the probability of surviving in University is made by the Kaplan-Meier estimator. Then, to test if there is a significant difference among the survival curves, Log-rank test is considered. Since it does not allow more than one explanatory variable to be taken into account, Cox Proportional Hazards model is used to analyse the interrelations between the covariates, and explored the influence of covariates on failure times. This study shows that there is a steep decline for students in Political Science and in Educational Studies, at the first year. Moreover, female students, students who attended "Licei" and those who completed their high school study with the highest mark (i.e. 110) have the highest probability of surviving at the University, i.e. the highest rate of taking the degree. In all these cases, it can be noted that there is a Faculty effect. These differences, tested by logrank test, are significant. Finally, Cox PH model for each Faculty confirms previous results

A class of nonparametric bivariate survival function estimators for randomly censored and truncated data

This paper proposes a class of nonparametric estimators for the bivariate survival function estimation under both random truncation and random censoring. In practice, the pair of random variables under consideration may have certain parametric relationship. The proposed class of nonparametric estimators uses such parametric information via a data transformation approach and thus provides more accurate estimates than existing methods without using such information. The large sample properties of the new class of estimators and a general guidance of how to find a good data transformation are given. The proposed method is also justified via a simulation study and an application on an economic data set.</p

Linear transformation models for censored data under truncation

In many observational cohort studies, a pair of correlated event times are usually observed for each individual. This paper develops a new approach for the semiparametric linear transformation model to handle the bivariate survival data under both truncation and censoring. By incorporating truncation, the potential referral bias in practice is taken into account. A class of generalised estimating equations are proposed to obtain unbiased estimates of the regression parameters. Large sample properties of the proposed estimator are provided. Simulation studies under different scenarios and analyses of real-world datasets are conducted to assess the performance of the proposed estimator

Variable selection in forecasting models for corporate bankruptcy

In this paper we develop statistical models for bankruptcy prediction of Italian firms in the limited liability sector, using annual balance sheet information. Several issues involved in default risk analysis are investigated, such as the structure of the data-base, the sampling procedure and the influence of predictors. In particular we focus on the variable selection problem, comparing innovative techniques based on shrinkage with traditional stepwise methods. The predictive performance of the proposed default risk model has been evaluated by means of different accuracy measures. The results of the analysis, carried out on a data-set of financial ratios expressly created from a sample of industrial firms annual reports, give evidence in favor of the proposed model over traditional ones

Variable Ranking In Bivariate Copula Survival Models

We propose a variable ranking procedure based on copula bivariate time- to-event margins under a general censoring scheme. The procedure identifies the im- portant variables influencing the two time-to-events in a high dimensional setting in- troducing a proper metric able to take into account the probabilistic copula structure. The proposal is the first attempt to apply a variable selection method to a copula bi- variate time-to-event domain. The advantages of the proposed approach are illustrated in a case study based on AREDS dataset

Truncation data analysis for the under-reporting probability in COVID-19 pandemic

The COVID-19 pandemic has affected all countries in the world and brings a major disruption in our daily lives. Estimation of the prevalence and contagiousness of COVID-19 infections may be challenging due to under-reporting of infected cases. For a better understanding of such pandemic in its early stages, it is crucial to take into consideration unreported infections. In this study we propose a truncation model to estimate the under-reporting probabilities for infected cases. Hypothesis testing on the differences in truncation probabilities, that are related to the under-reporting rates, is implemented. Large sample results of the hypothesis test are presented theoretically and by means of simulation studies. We also apply the methodology to COVID-19 data in certain countries, where under-reporting probabilities are expected to be high

Analisi delle strutture produttive e delle caratteristiche socio-economiche delle marine italiane

The aim of this paper is twofold. The first aim is to describe the geography and the Industrial structure of the Italian Fishing fleet. The second aim is to analyze their evolution during the period 2004-2013. The Italian fishery is one the most important of the Europe, and of the Mediterranean. It is multispecies and multifisheries and its geography is very complex. It has also been affected by many regulatory interventions that impacted its structure. We apply statistical univariate e multivariate methods to describe the structure and the dynamic of the Italian fleet and the link with the local economies

Clinical correlates of "pure" essential tremor: the TITAN study

BackgroundTo date, there are no large studies delineating the clinical correlates of "pure" essential tremor (ET) according to its new definition.MethodsFrom the ITAlian tremor Network (TITAN) database, we extracted data from patients with a diagnosis of "pure" ET and excluded those with other tremor classifications, including ET-plus, focal, and task-specific tremor, which were formerly considered parts of the ET spectrum.ResultsOut of 653 subjects recruited in the TITAN study by January 2022, the data of 208 (31.8%) "pure" ET patients (86M/122F) were analyzed. The distribution of age at onset was found to be bimodal. The proportion of familial cases by the age-at-onset class of 20 years showed significant differences, with sporadic cases representing the large majority of the class with an age at onset above 60 years. Patients with a positive family history of tremor had a younger onset and were more likely to have leg involvement than sporadic patients despite a similar disease duration. Early-onset and late-onset cases were different in terms of tremor distribution at onset and tremor severity, likely as a function of longer disease duration, yet without differences in terms of quality of life, which suggests a relatively benign progression. Treatment patterns and outcomes revealed that up to 40% of the sample was unsatisfied with the current pharmacological options.DiscussionThe findings reported in the study provide new insights, especially with regard to a possible inversed sex distribution, and to the genetic backgrounds of "pure" ET, given that familial cases were evenly distributed across age-at-onset classes of 20 years. Deep clinical profiling of "pure" ET, for instance, according to age at onset, might increase the clinical value of this syndrome in identifying pathogenetic hypotheses and therapeutic strategies

Clinical correlates of “pure” essential tremor: the TITAN study

BackgroundTo date, there are no large studies delineating the clinical correlates of “pure” essential tremor (ET) according to its new definition.MethodsFrom the ITAlian tremor Network (TITAN) database, we extracted data from patients with a diagnosis of “pure” ET and excluded those with other tremor classifications, including ET-plus, focal, and task-specific tremor, which were formerly considered parts of the ET spectrum.ResultsOut of 653 subjects recruited in the TITAN study by January 2022, the data of 208 (31.8%) “pure” ET patients (86M/122F) were analyzed. The distribution of age at onset was found to be bimodal. The proportion of familial cases by the age-at-onset class of 20 years showed significant differences, with sporadic cases representing the large majority of the class with an age at onset above 60 years. Patients with a positive family history of tremor had a younger onset and were more likely to have leg involvement than sporadic patients despite a similar disease duration. Early-onset and late-onset cases were different in terms of tremor distribution at onset and tremor severity, likely as a function of longer disease duration, yet without differences in terms of quality of life, which suggests a relatively benign progression. Treatment patterns and outcomes revealed that up to 40% of the sample was unsatisfied with the current pharmacological options.DiscussionThe findings reported in the study provide new insights, especially with regard to a possible inversed sex distribution, and to the genetic backgrounds of “pure” ET, given that familial cases were evenly distributed across age-at-onset classes of 20 years. Deep clinical profiling of “pure” ET, for instance, according to age at onset, might increase the clinical value of this syndrome in identifying pathogenetic hypotheses and therapeutic strategies

Illness-Death Model for Clustered Data

The aim of this paper is to develop the three-State Illness-Death model taking into account the clustered nature of data. For this purpose, the Frailty Model has been applied in Multi-State framework, assuming that the underlying process is timehomogeneous Markovian. The unknown parameters of the model have been estimated both in presence of complete observations and in case of censoring. Thus, the impact and the effects of censoring on the estimation have been analyzed. The relevance of the proposed model is illustrated by means of a simulation study, in order to validate the model assumptions and the performance of the proposed estimators