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.
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).
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|Item Type:||Journal Article|
|Keywords:||Classification , Finite mixture modelling, Latent class analysis, MCMC, Parkinson's disease, Uncertainty|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
Past > Schools > Mathematical Sciences
|Copyright Owner:||Copyright 2010 SAGE Publications|
|Deposited On:||09 Jun 2011 08:14|
|Last Modified:||06 Feb 2013 08:15|
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