title: Non-proportional odds multivariate logistic regression of ordinal family data.
abstract: Methods to examine whether genetic and/or environmental sources can account for the residual variation in ordinal family data usually assume proportional odds. However, standard software to fit the non-proportional odds model to ordinal family data is limited because the correlation structure of family data is more complex than for other types of clustered data. To perform these analyses we propose the non-proportional odds multivariate logistic regression model and take a simulation-based approach to model fitting using Markov chain Monte Carlo methods, such as partially collapsed Gibbs sampling and the Metropolis algorithm. We applied the proposed methodology to male pattern baldness data from the Victorian Family Heart Study.
authors.affiliation: Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, 207 Bouverie Street, Carlton, Victoria 3010, Australia.
authors.forename: Sophie G
authors.initials: SG
authors.lastname: Zaloumis
authors.forename: Katrina J
authors.initials: KJ
authors.lastname: Scurrah
authors.forename: Stephen B
authors.initials: SB
authors.lastname: Harrap
authors.forename: Justine A
authors.initials: JA
authors.lastname: Ellis
authors.forename: Lyle C
authors.initials: LC
authors.lastname: Gurrin
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods*; D005260:Female; D005838:Genotype; D006801:Humans; D016015:Logistic Models; D008297:Male; D008390:Markov Chains; D015999:Multivariate Analysis; D010375:Pedigree; D010641:Phenotype
keywords: Correlated ordinal outcomes; Data augmentation; MCMC algorithm; Non-proportional odds; Partially collapsed Gibbs sampling