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Analysis of paediatric visual acuity using Bayesian copula models with sinh-arcsinh marginal densities

Cortina Borja, M; Wade, A; Stander, J; Dalla Valle, L; Liseo, B; Taglioni, C; (2020) Analysis of paediatric visual acuity using Bayesian copula models with sinh-arcsinh marginal densities. Statistics in Medicine , 38 (18) pp. 3424-3443. 10.1002/sim.8176. Green open access

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Abstract

We analyse paediatric ophthalmic data from a large sample of children aged between 3 and 8 years. We modify the Bayesian additive conditional bivariate copula regression model of Klein and Kneib [1] by using 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 the two-step frequentist generalized additive modelling approach of Vatter and Chavez-Demoulin [2].

Type: Article
Title: Analysis of paediatric visual acuity using Bayesian copula models with sinh-arcsinh marginal densities
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/sim.8176
Publisher version: https://doi.org/10.1002/sim.8176
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Bayesian dependence modelling, Conditional copulas, Generalized additive models for location, scale and shape (gamlss), Sinh-arcsinh distributions, Visual acuity
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Population, Policy and Practice Dept
URI: https://discovery.ucl.ac.uk/id/eprint/10074431
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