Stefano Bonnini

Temporomandibular joint osteoarthritis: an open label trial of 76 patients treated with arthrocentesis plus hyaluronic acid injections

This study is an open-label trial on a sample of 76 consecutive patients with temporomandibular joint (TMJ) osteoarthritis treated with a cycle of five weekly arthrocenteses plus hyaluronic acid injections. Patients had a diagnosis of osteoarthritis according to the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD Axis I Group IIIb). They underwent a cycle of five arthrocenteses with injections (1 per week) of 1ml hyaluronic acid and four follow-up assessments after the end of the treatment (at 1 week, 1 month, 3 months, 6 months). At each appointment, several subjective and objective outcome variables were assessed to test the efficacy of the treatment protocol. Marked improvements were reported for all variables during the treatment phase. The improvements were maintained over the 6-month follow-up period. The p-value of the multivariate permutation test for the efficacy of the treatment over time (with Tippetts combination) was 0.001, and significant changes at the end of the follow-up period were detected for almost all the outcome variables. Data from this study lend further support to the usefulness of serial hyaluronic acid injections performed after arthrocentesis for the treatment of TMJ osteoarthritis and for the maintenance of improvements over a 6-month follow-up period.

Personality characteristics and quality of life in patients treated for Cushing's syndrome

Objective  Psychological distress does not always disappear upon proper endocrine treatment of Cushings syndrome, and quality of life may still be compromised. Little is known on the personality correlates that may be involved. The aim of this study was to provide a controlled assessment of personality characteristics and quality of life in patients successfully treated for Cushings syndrome.

Treatment Effectiveness of Arthrocentesis Plus Hyaluronic Acid Injections in Different Age Groups of Patients With Temporomandibular Joint Osteoarthritis

PURPOSE To investigate for treatment effectiveness in different age groups of patients with temporomandibular joint osteoarthritis who underwent a cycle of 5 weekly arthrocenteses plus hyaluronic acid injections. MATERIALS AND METHODS We implemented a retrospective study on 76 patients followed up for 1 year. Outcome variables were pain levels at rest and during chewing, subjective masticatory efficiency, functional limitation, perceived efficacy, and jaw range of motion. Three age groups of patients were identified, and treatment effectiveness was compared among groups by means of a multistrata permutation test. RESULTS All the partial P values of the subtests related to the age groups, adjusted according to the close testing method for controlling multiplicity, were significant: P = .009 (aged <45 years), P = .001 (aged 45-65 years), and P = .001 (aged >65 years). For the younger age group, the treatment had a significant effect only on the pain at mastication and on the subjective efficacy. For the other age groups, the treatment effectiveness was evident with regard to almost all the considered symptoms. CONCLUSIONS Our findings suggested that the treatment protocol was more effective in patients older than 45 years, thus having important clinical implications regarding attempts to define tailored treatment protocols for patients with temporomandibular disorders.

Nonparametric Hypothesis Testing: Rank and Permutation Methods with Applications in R

Description: A novel presentation of rank and permutation tests, with accessible guidance to applications in R Nonparametric testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. This book summarizes traditional rank techniques and more recent developments in permutation testing as robust tools for dealing with complex data with low sample size. Key Features: Examines the most widely used methodologies of nonparametric testing. Includes extensive software codes in R featuring worked examples, and uses real case studies from both experimental and observational studies. Presents and discusses solutions to the most important and frequently encountered real problems in different fields. Features a supporting website containing all of the data sets examined in the book along with ready to use R software codes. Nonparametric Hypothesis Testing combines an up to date overview with useful practical guidance to applications in R, and will be a valuable resource for practitioners and researchers working in a wide range of scientific fields including engineering, biostatistics, psychology and medicine.

Iterated combination-based paired permutation tests to determine shape effects of chemotherapy in patients with esophageal cancer

The nonparametric combination of dependent permutation tests method is a useful general tool when a testing problem can be broken down into a set of different k > 1 partial tests. These partial tests, after adjustment of p-values to control for multiplicity, can be marginally analyzed, but jointly considered they can provide information on an overall hypothesis, which might represent the true goal of the testing problem. On the one hand, independence among the partial tests is usually an unrealistic assumption; on the other, even when the underlying dependence relations are known quite often they are difficult to cope with properly. Therefore this combination must be achieved nonparametrically, by implicitly taking into account the dependence structure of tests without explicitly describing it. An important property of the tests based on nonparametric combination methodology, when the number of response variables is high compared to the sample sizes, consists in the finite sample consistency. A practical problem involves choosing the most suitable combining function for each specific testing problem given that the final result can be affected by this crucial choice. The purpose of this article is to present an nonparametric combination solution based on the iterated combination of partial tests, evaluate its power behavior using a Monte Carlo simulation study and apply it to a real medical problem, namely the evaluation of the effects of chemotherapy on the shape of esophageal tumors. R code has been implemented to carry out the analyses.

A permutation approach for testing heterogeneity in two-sample categorical variables

In many sciences researchers often meet the problem of establishing if the distribution of a categorical variable is more concentrated, or less heterogeneous, in population P1 than in population P2. An approximate nonparametric solution to this problem is discussed within the permutation context. Such a solution has similarities to that of testing for stochastic dominance, that is, of testing under order restrictions, for ordered categorical variables. Main properties of given solution and a Monte Carlo simulation in order to evaluate its degree of approximation and its power behaviour are examined. Two application examples are also discussed.

Employment status and education/employment relationship of PhD graduates from the University of Ferrara

Two sample surveys of Post-Docs were planned and carried out at the University of Ferrara in 2004 and 2007 aimed at determining the professional status of Post-Docs, the relationship between their PhD education and employment, and their satisfaction with certain aspects of the education and research program. As part of these surveys, two methodological contributions were developed. The first concerns an extension of the non-parametric combination of dependent rankings to construct a synthesis of composite indicators measuring satisfaction with particular aspects of PhD programs [R. Arboretti Giancristofaro and L. Salmaso, Global ranking indicators with application to the evaluation of PhD programs, Atti del Convegno “Valutazione e Customer Satisfaction per la Qualità dei Servizi”, Roma, 8–9 Settembre 2005, pp. 19–22; R. Arboretti Giancristofaro, S. Bonnini, and L. Salmaso, A performance indicator for multivariate data, Quad. Stat. 9 (2007), pp. 1–29; R. Arboretti Giancristofaro, F. Pesarin, and L. Salmaso, Nonparametric approaches for multivariate testing with mixed variables and for ranking on ordered categorical variables with an application to the evaluation of PhD programs, in Real Data Analysis, S. Sawilowsky, ed., a volume in Quantitative Methods in Education and the Behavioral Sciences: Issues, Research and Teaching, Ronald C. Serlin, series ed., Information Age Publishing, Charlotte, North Carolina, 2007, pp. 355–385]. The procedure was applied to highlight differences in the interviewed Post-Docs’ multivariate satisfaction profiles in relation to two aspects: education/employment relationship; employment expectations; and opportunities. The second consists of an inferential procedure providing a solution to the problem of hypothesis testing, where the objective is to compare the heterogeneity of two populations on the basis of sampling data [G.R. Arboretti, S. Bonnini, and F. Pesarin, A permutation approach for testing heterogeneity in two-sample categorical variables, Stat. Comput. (2009) doi: 10.1007/S11222-008-9085-8.]. The procedure was applied to compare the degrees of heterogeneity of Post-Doc judgments in the two surveys with regard to the adequacy of the PhD education for the work carried out.

Moment-based multivariate permutation tests for ordinal categorical data

Stochastic dominance problems in testing for ordered categorical variables are of specific interest in performance analysis, because they are frequently encountered in practice and present distinctive difficulties, especially within the framework of likelihood ratio tests. Until now, the literature has essentially considered the univariate case, and several solutions have been proposed to cope with it, most of which are based on restricted maximum likelihood ratio tests. These solutions are generally criticised, because the degree of accuracy of their asymptotic null and alternative distributions is difficult to assess and characterise. In this paper, we propose a new exact solution based on a simultaneous analysis of a finite set of sampling moments of ranks, or general scores, assigned to ordered classes and processed within a permutation approach.

Testing for Heterogeneity with Categorical Data: Permutation Solution vs. Bootstrap Method

In this article the problem of comparing distributional heterogeneities for categorical variables is addressed. Specifically, the one-sided testing problem for heterogeneity comparisons is considered. For such a problem a bootstrap method is proposed and compared with a permutation method already present in literature. The power behavior of the two methods is compared through a Monte Carlo simulation study. The results of two real applications are shown.

Permutation Approaches for Stochastic Ordering

In many application problems, when dealing with comparisons between two or more groups, the classical parametric inferential statistical methods are used, although in real problems the quite stringent assumptions required by such methods are rarely satisfied. In particular a parametric approach to the test on ordering of C > 2 populations is very difficult. In order to tackle this problem two alternative methods are proposed in the present paper. Both the methods consist in permutation combination based tests: the first is supposed to be more powerful and it is suitable when the main goal of the study is related to the global ordering of the populations; the second is useful when the interest is in the pairwise comparisons between the populations.