Pietro Amenta

Two Approaches for Discriminant Partial Least Squares

In the medical sciences as well as in other contexts we often have to deal with the study of groups and with the research of their separation. The aim of this paper is to highlight how, in some situations, Partial Least Squares (PLS) Discriminant Analysis (Sjostrom et al., 1986) can lead to a solution that is not an answer to the given problem of discrimination. Within a PLS framework, the authors provide two extensions of it. The first is close to the Generalized PLS proposed by (1997) but used in the discrimination context. The second proposal, in the same framework, leads to consider the PLS Redundancy Analysis proposed by (1995) by using suitable metrics. Some examples of data treatment are given.

Catanova for two-way contingency tables with ordinal variables using orthogonal polynomials

ABSTRACT The analysis of variance of cross-classified (categorical) data (CATANOVA) is a technique designed to identify the variation between treatments of interest to the researcher. There are well-established links between CATANOVA and the Goodman and Kruskal tau statistic as well as the Light and Margolin R 2 for the purposes of the graphical identification of this variation. The aim of this article is to present a partition of the numerator of the tau statistic, or equivalently, the BSS measure in the CATANOVA framework, into location, dispersion, and higher order components. Even if a CATANOVA identifies an overall lack of variation, by considering this partition and calculations derived from them, it is possible to identify hidden, but statistically significant, sources of variation.

Determinants of patient satisfaction: a systematic review

Aim: A large number of studies have addressed the detection of patient satisfaction determinants, and the results are still inconclusive. Furthermore, it is known that contradicting evidence exists across patient satisfaction studies. This article is the second part of a two-part series of research with a goal to review a current conceptual framework of patient satisfaction for further operationalisation procedures. The aim of this work was to systematically identify and review evidence regarding determinants of patient satisfaction between 1980 and 2014, and to seek the reasons for contradicting results in relationships between determinants and patient satisfaction in the literature to design a further robust measurement system for patient satisfaction. Method: This systematic review followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. The search was conducted in PubMed, CINAHL, and Scopus in October 2014. Studies published in full in peer reviewed journals between January 1980 and August 2014 and in the English language were included. We included 109 articles for the synthesis. Results: We found several number of determinants of patient satisfaction investigated in a wide diversity of studies. However, study results were varied due to no globally accepted formulation of patient satisfaction and measurement system. Conclusions: Health care service quality indicators were the most influential determinants of patient satisfaction across the studies. Among them, health providers’ interpersonal care quality was the essential determinant of patient satisfaction. Sociodemographic characteristics were the most varied in the review. The strength and directions of associations with patient satisfaction were found inconsistent. Therefore, person-related characteristics should be considered to be the potential determinants and confounders simultaneously. The selected studies were not able to show all potential characteristics which may have had effects on satisfaction. There is a need for more studies on how cultural, behavioural, and socio-demographic differences affect patient satisfaction, using a standardised questionnaire.

Conceptualisation of patient satisfaction: a systematic narrative literature review

Aim: Patient satisfaction concept is widely measured due to its appropriateness to health service; however, evidence suggests that it is a poorly developed concept. This article is a first part of a two-part series of research with a goal to review a current conceptual framework of patient satisfaction and to bring the concept for further operationalisation procedures. The current article aimed to review a theoretical framework that helps the next article to review determinants of patient satisfaction for designing a measurement system. Method: The study used a systematic review method, meta-narrative review, based on the RAMESES guideline with the phases of screening evidence, appraisal evidence, data extraction and synthesis. Patient satisfaction theoretical articles were searched on the two databases MEDLINE and CINAHL. Inclusion criteria were articles published between 1980 and 2014, and English language papers only. There were 36 articles selected for the synthesis. Results: Results showed that most of the patient satisfaction theories and formulations are based on marketing theories and defined as how well health service fulfils patient expectations. However, review demonstrated that a relationship between expectation and satisfaction is unclear and the concept expectation itself is not distinctly theorised as well. Conclusions: Researchers brought satisfaction theories from other fields to the current healthcare literature without much adaptation. Thus, there is a need to attempt to define the patient satisfaction concept from other perspectives or to learn how patients evaluate the care rather than struggling to describe it by consumerist theories.

Restricted Co-inertia Analysis

In this paper, an extension of the Co-inertia Analysis is proposed. This extension is based on a objective function which takes into account directly the external information, as linear restrictions about one set of variables, by rewriting the Co-inertia Analysis objective function according to the principle of Restricted Eigenvalue Problem (Rao (1973)).

Generalized Constrained Principal Component Analysis

This paper deals with a non-symmetrical analysis of two multiple data sets in order to study the structure of dependence among sets of variables which play different role in the analysis. This approach represents a generalization of the Constrained Principal Component Analysis (CPCA) (D’Ambra and Lauro, 1982).

Decomposition of cumulative chi-squared statistics, with some new tools for their interpretation

It is well known that the Pearson statistic

Sensory analysis via multi-block multivariate additive PLS splines

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