Journal article
Non-proportional odds multivariate logistic regression of ordinal family data
SG Zaloumis, KJ Scurrah, SB Harrap, JA Ellis, LC Gurrin
Biometrical Journal | WILEY-BLACKWELL | Published : 2015
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 ..
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Funding Acknowledgements
The authors would like to thank Dr Sean M. O'Brien for providing the data analysed in the application section of his and Dr David B. Dunson's paper (O'Brien and Dunson, 2004), which aided in the development of an R program to implement the MCMC algorithm in Section 5.3. We would also like to thank the Victorian Family Heart Study for the baldness and SNP data. Dr Katrina J. Scurrah and Dr Lyle C. Gurrin were funded by the National Health and Medial Research Council of Australia.