Objective-To establish the frequency of connective tissue diseases (CTD) in a cohort of Italian patients with primary biliary cirrhosis (PBC) and to evaluate the availability of a marker for the early identification of the more common associated CTD. Methods-One hundred and seventy consecutive patients with histologically diagnosed PBC were screened for the presence of a CTD and/or Raynaud's phenomenon (RP). Patients were classified as having a CTD only if they fulfilled standardised criteria. Results-Forty seven patients had a CTD. The most common CTD was systemic sclerosis (SSc), found in 21 patients. RP was present in 54 patients, most of whom (n=39) had an associated CTD. The most prevalent autoantibodies included antinuclear antibodies (ANA) with anticentromere (ACA) and speckled patterns (34 and 33 patients, respectively) and extractable nuclear antigens (ENA, 27 patients). However, while the frequencies of ACA and ENA were significantly higher in patients with an associated CTD (p<0.0001 and p<0.005, respectively), no relationship was found for speckled ANA. ACA was the best predictor of a CTD in patients with PBC (odds ratio (OR) 24.5, 95% CI 5.5 to 108.8), followed by the presence of ENA (OR 23.9, 95% CI 5.6 to 101.0) and RP (OR 20.2, 95% CI 5.7 to 71.2). Conclusions-Using strict standardised classification criteria we have found that SSc is the most common CTD associated with PBC and that ACA and ENA are strong markers for an associated CTD in patients with PBC.

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.

The age-period-cohort analysis allows targeting of health care and prevention programmes based on future trends. Aetiological and prognostic factors act differently in Europe. A better understanding of the trends would require more detailed information on alcoholism treatment rates, alcohol habits, viral hepatitic infections and other factors involved in the aetiopathogenesis of the disease.

No abstract

No abstract

No abstract

No abstract

This article focuses on a statistical tool for dependence analysis in scientific research. Starting from a recent index of concordance for a multiple linear regression model, a coefficient suitable in catching any monotonic dependence relationship between a dependent variable and an independent variable is derived and discussed. Given its interpretation in terms of monotonic dependence, it is called monotonic dependence coefficient (MDC). It is appropriate to all contexts where the dependent variable is quantitative (continuous or discrete) and the independent variable is at least of ordinal nature; tied data are also allowed. MDC's adequacy is validated through Monte Carlo simulations led by taking into account different scenarios of dependence. Finally, an application to real data is provided to stress MDC's capability of detecting dependence relationships between two variables, even if some pieces of information about original data are lost.