Pier Alda Ferrari

Simulating Ordinal Data

The increasing use of ordinal variables in different fields has led to the introduction of new statistical methods for their analysis. The performance of these methods needs to be investigated under a number of experimental conditions. Procedures to simulate from ordinal variables are then required. In this article, we deal with simulation from multivariate ordinal random variables. We propose a new procedure for generating samples from ordinal random variables with a prespecified correlation matrix and marginal distributions. Its features are examined and compared with those of its main competitors. A software implementation in R is also provided along with examples of its application.

Exploring the combined action of lifetime alcohol intake and chronic hepatotropic virus infections on the risk of symptomatic liver cirrhosis

Although alcohol intake and hepatitis B and C virus (HBV and HCV) infections are the major determinants of liver cirrhosis (LC) in western countries, the joint effect of these factors on LC risk has not yet been adequately studied. Data from three case-control studies performed in Italy were used. Cases were 462 cirrhotic patients admitted to Hospitals for liver decompensation. Controls were 651 inpatients admitted for acute diseases unrelated to alcohol. Alcohol consumption was expressed as lifetime daily alcohol intake (LDAI). Three approaches were used to explore the interaction structure. The Breslow and Storer parametric family of relative risk functions showed that an intermediate structure of interaction from additive to multiplicative was the most adequate one. The Rothman synergism index showed that the interaction structure between LDAI and viral status differed significantly from the additive model in particular for high levels of alcohol intake. When multiple regression additive and multiplicative models were compared after adjustment for the known confounding variables, a trend of the interaction structure towards the multiplicative model was observed at increasing levels of consumption. Better methods are needed for assessing mixed interaction structures in conditions characterized by multifactorial etiologies like cirrhosis of the liver.

An imputation method for categorical variables with application to nonlinear principal component analysis

The problem of missing data in building multidimensional composite indicators is a delicate problem which is often underrated. An imputation method particularly suitable for categorical data is proposed. This method is discussed in detail in the framework of nonlinear principal component analysis and compared to other missing data treatments which are commonly used in this analysis. Its performance vs. these other methods is evaluated throughout a simulation procedure performed on both an artificial case, varying the experimental conditions, and a real case. The proposed procedure is implemented using R.

Thyroid dysfunction in women with systemic sclerosis

Abstract:  Hypothyroidism has been frequently reported in systemic sclerosis (SSc), but whether SSc itself increases the risk of thyroid dysfunction is still controversial. The aim of the present study was to determine whether routine thyroid function screening in SSc should be warranted. Serum levels of free triiodothyronine, free thyroxine, and thyroid‐stimulating hormone, and the presence of thyroid‐specific autoantibodies (antithyroid peroxidase and antithyreoglobulin) were measured in 79 women with SSc and 81 age‐matched women with osteoarthritis (OA) serving as controls. Hyperthyroidism was found in 2 of 79 (2.5%) SSc and in 4 of 81 (5%) OA cases. Hypothyroidism was found in 16 of 79 (20%) patients with SSc (subclinical in 14/16 cases) and in 9 of 81 (11%) patients with OA (subclinical in all cases; P= 0.131). Antithyreoglobulin antibodies were present in 14% versus 13% patients (SSc versus OA, P= NS), whereas antithyroid peroxidase antibodies were present in 23% versus 11% patients (SSc versus OA, P= 0.057). The risk of hypothyroidism was significantly higher in antithyroid peroxidase‐positive patients (P < 0.0001), irrespective of the primary diagnosis, and greater in women with OA (OR = 24.6, 95% CI 4.3–141.9, P < 0.0001) than SSc (OR = 4.2, 95% CI 1.2–14.3, P= 0.035). SSc is not independently associated with an increased risk of thyroid dysfunction, but antithyroid peroxidase antibodies may identify a subset of patients at risk of developing thyroid dysfunction.

Citizens evaluate public services: a critical overview of statistical methods for analysing user satisfaction

Public enterprises may be unaware of their performance in providing services. In situations where citizens cannot switch to other providers or reduce the use of the service, the evaluation of users’ satisfaction becomes a very important topic. At the same time, this is a tricky task, given the particular nature of this variable. Appropriate statistical methods to assess and explain the level of satisfaction are useful tools to face these issues. In this paper, we analyse some of these methods and their potential in giving advice to public managers to improve citizens’ satisfaction.

Measuring Service Quality: The Opinion of Europeans About Utilities

This paper provides a comparative analysis of statistical methods to evaluate the consumer perception about the quality of Services of General Interest. The evaluation of the service quality perceived by users is usually based on Customer Satisfaction Survey data and an ex-post evaluation is then performed. Another approach, consisting in evaluating Consumers preferences, supplies an ex-ante information on Service Quality. Here, the ex-post approach is considered, two non-standard techniques - the Rasch Model and the Nonlinear Principal Component Analysis - are presented and the potential of both methods is discussed. These methods are applied on the Eurobarometer Survey data to assess the consumer satisfaction among European countries and in different years.

A sequential distance-based approach for imputing missing data: Forward Imputation

Missing data recurrently affect datasets in almost every field of quantitative research. The subject is vast and complex and has originated a literature rich in very different approaches to the problem. Within an exploratory framework, distance-based methods such as nearest-neighbour imputation (NNI), or procedures involving multivariate data analysis (MVDA) techniques seem to treat the problem properly. In NNI, the metric and the number of donors can be chosen at will. MVDA-based procedures expressly account for variable associations. The new approach proposed here, called Forward Imputation, ideally meets these features. It is designed as a sequential procedure that imputes missing data in a step-by-step process involving subsets of units according to their “completeness rate”. Two methods within this context are developed for the imputation of quantitative data. One applies NNI with the Mahalanobis distance, the other combines NNI and principal component analysis. Statistical properties of the two methods are discussed, and their performance is assessed, also in comparison with alternative imputation methods. To this purpose, a simulation study in the presence of different data patterns along with an application to real data are carried out, and practical hints for users are also provided.

Missing data in optimal scaling

We propose a procedure to assess a measure for a latent phenomenon, starting from the observation of a wide set of ordinal variables affected by missing data. The proposal is based on Nonlinear PCA technique to be jointly used with an ad hoc imputation method for the treatment of missing data. The procedure is particularly suitable when dealing with ordinal, or mixed, variables, which are strongly interrelated and in the presence of Specific patterns of missing observations.

An R package for the simulation of correlated discrete variables

ABSTRACT A package for the stochastic simulation of discrete variables with assigned marginal distributions and correlation matrix is presented and discussed. The simulating mechanism relies upon the Gaussian copula, linking the discrete distributions together, and an iterative scheme recovering the correlation matrix for the copula that ensures the desired correlations among the discrete variables. Examples of its use are provided as well as three possible applications (related to probability, sampling, and inference), which illustrate the utility of the package as an efficient and easy-to-use tool both in statistical research and for didactic purposes.

Multidimensional item response theory models for dichotomous data in customer satisfaction evaluation

In this paper, multidimensional item response theory models for dichotomous data, developed in the fields of psychometrics and ability assessment, are discussed in connection with the problem of evaluating customer satisfaction. These models allow us to take into account latent constructs at various degrees of complexity and provide interesting new perspectives for services quality assessment. Markov chain Monte Carlo techniques are considered for estimation. An application to a real data set is also presented.