Analysis of paediatric visual acuity using Bayesian copula models with sinh‐arcsinh marginal densities. (29th May 2019)

• Record Type: Journal Article
• Authors: Stander, Julian; Dalla Valle, Luciana; Taglioni, Charlotte; Liseo, Brunero; Wade, Angie; Cortina‐Borja, Mario
• Is Part Of: Statistics in medicine. Volume 38:Number 18(2019)
• Page Start: 3421
• Page End: 3443
• Publication Date: 2019-05-29
• Subjects: Bayesian dependence modelling -- conditional copulas -- generalised additive models for location -- scale and shape (gamlss) -- sinh‐arcsinh distribution -- visual acuity; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8176
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : We analyse paediatric ophthalmic data from a large sample of children aged between 3 and 8 years. We use a Bayesian additive conditional bivariate copula regression model with sinh‐arcsinh marginal densities with location, scale, and shape parameters that depend smoothly on a covariate. We perform Bayesian inference about the unknown quantities of our model using a specially tailored Markov chain Monte Carlo algorithm. We gain new insights about the processes, which determine transformations in visual acuity with respect to age, including the nature of joint changes in both eyes as modelled with the age‐related copula dependence parameter. We analyse posterior predictive distributions to identify children with unusual sight characteristics, distinguishing those who are bivariate, but not univariate outliers. In this way, we provide an innovative tool that enables clinicians to identify children with unusual sight who may otherwise be missed. We compare our simultaneous Bayesian method with a two‐step frequentist generalised additive modelling approach.