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Non-proportional odds multivariate logistic regression of ordinal family data. (English) Zbl 1310.62121

Summary: 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.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
62P25 Applications of statistics to social sciences

Software:

SAS/STAT; R; SAS; GLLAMM; Stata
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References:

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