title: Probabilistic subgroup identification using Bayesian finite mixture modelling: a case study in Parkinson's disease phenotype identification.
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).
authors.affiliation: Mathematical Sciences, Queensland University of Technology, Brisbane, Australia. nm.white@qut.edu.au
authors.forename: Nicole
authors.initials: N
authors.lastname: White
authors.forename: Helen
authors.initials: H
authors.lastname: Johnson
authors.forename: Peter
authors.initials: P
authors.lastname: Silburn
authors.forename: George
authors.initials: G
authors.lastname: Mellick
authors.forename: Nadeeka
authors.initials: N
authors.lastname: Dissanayaka
authors.forename: Kerrie
authors.initials: K
authors.lastname: Mengersen
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D015233:Models, Statistical; D010300:Parkinson Disease / Q000473:pathology*; D010641:Phenotype; D035501:Uncertainty