Addressing missing covariates for the regression analysis of competing risks: Prognostic modelling for triaging patients diagnosed with prostate cancer.

Competing risks arise in medical research when subjects are exposed to various types or causes of death. Data from large cohort studies usually exhibit subsets of regressors that are missing for some study subjects. Furthermore, such studies often give rise to censored data.

Microcultures of lactic acid bacteria: characterization and selection of strains, optimization of nutrients and gallic acid concentration.

Eighteen lactic acid bacteria (LAB) strains, isolated from coffee pulp silages were characterized according to both growth and gallic acid (GA) consumption. Prussian blue method was adapted to 96-well microplates to quantify GA in LAB microcultures. Normalized data of growth and GA consumption were used to characterize strains into four phenotypes.

A flexible class of parametric transition regression models based on copulas: application to poliomyelitis incidence.

This paper presents an extension of a general parametric class of transitional models of order p. In these models, the conditional distribution of the current observation, given the present and past history, is a mixture of conditional distributions, each of them corresponding to the current observation, given each one of the p-lagged observations. Such conditional distributions are constructed using bivariate copula models which allow for a rich range of dependence suitable to model non-Gaussian time series.