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Variable Selection in Covariate Dependent Random Partition Models: an Application to Urinary Tract Infection

Barcella, W; Iorio, MD; Baio, G; Malone-Lee, J; (2016) Variable Selection in Covariate Dependent Random Partition Models: an Application to Urinary Tract Infection. Statistics in Medicine , 35 (8) pp. 1373-1389. 10.1002/sim.6786. Green open access

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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.

Type: Article
Title: Variable Selection in Covariate Dependent Random Partition Models: an Application to Urinary Tract Infection
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/sim.6786
Publisher version: http://dx.doi.org/10.1002/sim.6786
Language: English
Additional information: This is the peer reviewed version of the following article: Barcella, W; Iorio, MD; Baio, G; Malone-Lee, J; (2016) Variable Selection in Covariate Dependent Random Partition Models: an Application to Urinary Tract Infection. Statistics in Medicine, 35 (8) pp. 1373-1389, which has been published in final form at: http://dx.doi.org/10.1002/sim.6786. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Keywords: Bayesian nonparametrics; clustering; variable selection; Dirichlet process; spike and slab priors
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Renal Medicine
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/1461489
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