Variable selection in covariate dependent random partition models: an application to urinary tract infection.

Lower urinary tract symptoms can indicate the presence of urinary tract infection (UTI), a condition that if it becomes chronic requires expensive and time consuming care as well as leading to reduced quality of life. Detecting the presence and gravity of an infection from the earliest symptoms is then highly valuable. Typically, white blood cell (WBC) count measured in a sample of urine is used to assess UTI. We consider clinical data from 1341 patients in their first visit in which UTI (i.e. WBC ≥ 1) is diagnosed. In addition, for each patient, a clinical profile of 34 symptoms was recorded. In this paper, we propose a Bayesian nonparametric regression model based on the Dirichlet process prior aimed at providing the clinicians with a meaningful clustering of the patients based on both the WBC (response variable) and possible patterns within the symptoms profiles (covariates). This is achieved by assuming a probability model for the symptoms as well as for the response variable. To identify the symptoms most associated to UTI, we specify a spike and slab base measure for the regression coefficients: this induces dependence of symptoms selection on cluster assignment. Posterior inference is performed through Markov Chain Monte Carlo methods.

Variance and covariance heterogeneity analysis for detection of metabolites associated with cadmium exposure.

In this study, we propose a novel statistical framework for detecting progressive changes in molecular traits as response to a pathogenic stimulus. In particular, we propose to employ Bayesian hierarchical models to analyse changes in mean level, variance and correlation of metabolic traits in relation to covariates. To illustrate our approach we investigate changes in urinary metabolic traits in response to cadmium exposure, a toxic environmental pollutant. With the application of the proposed approach, previously unreported variations in the metabolism of urinary metabolites in relation to urinary cadmium were identified. Our analysis highlights the potential effect of urinary cadmium on the variance and correlation of a number of metabolites involved in the metabolism of choline as well as changes in urinary alanine. The results illustrate the potential of the proposed approach to investigate the gradual effect of pathogenic stimulus in molecular traits.