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

White, Nicole, Johnson, Helen, Silburn, Peter, Mellick, George, Dissanayaka, Nadeeka and Mengersen, Kerrie (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: 563-583. doi:10.1177/0962280210391012


Author White, Nicole
Johnson, Helen
Silburn, Peter
Mellick, George
Dissanayaka, Nadeeka
Mengersen, Kerrie
Total Author Count Override 6
Title Probabilistic subgroup identification using Bayesian finite mixture modelling: a case study in Parkinson's disease phenotype identification
Journal name Statistical Methods in Medical Research   Check publisher's open access policy
ISSN 0962-2802
1477-0334
Publication date 2012-12
Year available 2010
Sub-type Article (original research)
DOI 10.1177/0962280210391012
Volume 21
Issue 6
Start page 563
End page 583
Total pages 21
Place of publication London, United Kingdom
Publisher Sage Publications
Collection year 2013
Language eng
Formatted abstract
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).
Keyword Classification
Finite mixture modelling
Latent class analysis
MCMC
Parkinson’s disease
Uncertainty
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online before print December 16, 2010.

Document type: Journal Article
Sub-type: Article (original research)
Collections: UQ Centre for Clinical Research Publications
Official 2013 Collection
ERA 2012 Admin Only
 
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Created: Thu, 02 Feb 2012, 11:10:18 EST by Roheen Gill on behalf of UQ Centre for Clinical Research