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Stat Methods Med Res. 2016 Apr;25(2):644-58. doi: 10.1177/0962280212463415. Epub 2012 Oct 14.

A Bayesian semiparametric approach with change points for spatial ordinal data.

Author information

1
Department of Epidemiology and Biostatistics, University of South Carolina, USA bcai@sc.edu.
2
Division of Biostatistics & Epidemiology, Medical University of South Carolina, USA.
3
Department of Family and Preventive Medicine, University of South Carolina, USA.
4
School of Public Health and Health Sciences, University of Massachusetts, Amherst, USA.

Abstract

The change-point model has drawn much attention over the past few decades. It can accommodate the jump process, which allows for changes of the effects before and after the change point. Intellectual disability is a long-term disability that impacts performance in cognitive aspects of life and usually has its onset prior to birth. Among many potential causes, soil chemical exposures are associated with the risk of intellectual disability in children. Motivated by a study for soil metal effects on intellectual disability, we propose a Bayesian hierarchical spatial model with change points for spatial ordinal data to detect the unknown threshold effects. The spatial continuous latent variable underlying the spatial ordinal outcome is modeled by the multivariate Gaussian process, which captures spatial variation and is centered at the nonlinear mean. The mean function is modeled by using the penalized smoothing splines for some covariates with unknown change points and the linear regression for the others. Some identifiability constraints are used to define the latent variable. A simulation example is presented to evaluate the performance of the proposed approach with the competing models. A retrospective cohort study for intellectual disability in South Carolina is used as an illustration.

KEYWORDS:

Bayesian semiparametric model; change point; intellectual disability; regression splines; soil metal exposure

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