Probabilistic subgroup identification using Bayesian finite mixture modelling : a case study in Parkinson’s disease phenotype identification

White, Nicole, Johnson, Helen, Silburn, Peter A., Mellick, George, Dissanayaka, Nadeeka, & Mengersen, Kerrie L. (2012) Probabilistic subgroup identification using Bayesian finite mixture modelling : a case study in Parkinson’s disease phenotype identification. Statistical Methods in Medical Research, 21(6), pp. 563-583.

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This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty:

(i) as a means of describing a person’s membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation.

These proposed uses are demonstrated on a case study in Parkinson’s disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinson’s Disease Rating Scale (UPDRS).

Impact and interest:

2 citations in Scopus
3 citations in Web of Science®
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ID Code: 41940
Item Type: Journal Article
Refereed: Yes
Keywords: Classification , Finite mixture modelling, Latent class analysis, MCMC, Parkinson's disease, Uncertainty
DOI: 10.1177/0962280210391012
ISSN: 0962-2802
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2010 SAGE Publications
Deposited On: 08 Jun 2011 22:14
Last Modified: 25 Jun 2017 14:41

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