title: Variable selection in covariate dependent random partition models: an application to urinary tract infection.
abstract: 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.
authors.affiliation: Department of Statistical Science, University College London, London, U.K.
authors.forename: William
authors.initials: W
authors.lastname: Barcella
authors.affiliation: Department of Statistical Science, University College London, London, U.K.
authors.forename: Maria De
authors.initials: MD
authors.lastname: Iorio
authors.affiliation: Department of Statistical Science, University College London, London, U.K.
authors.forename: Gianluca
authors.identifier: http://orcid.org/0000-0003-4314-2570
authors.initials: G
authors.lastname: Baio
authors.affiliation: University College London Medical School, London, U.K.
authors.forename: James
authors.initials: J
authors.lastname: Malone-Lee
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D056808:Biostatistics; D006801:Humans; D007962:Leukocytes / Q000473:pathology; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D012044:Regression Analysis; D018709:Statistics, Nonparametric; D014552:Urinary Tract Infections / Q000175:diagnosis* / Q000652:urine
keywords: Bayesian nonparametrics; Dirichlet process; clustering; spike and slab priors; variable selection