Comparing predictive abilities of longitudinal child growth models

Anderson, C. , Hafen, R., Sofrygin, O. and Ryan, L. (2019) Comparing predictive abilities of longitudinal child growth models. Statistics in Medicine, 38(19), pp. 3555-3570. (doi:10.1002/sim.7693) (PMID:30094965)

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Abstract

The Bill and Melinda Gates Foundation's Healthy Birth, Growth and Development knowledge integration project aims to improve the overall health and well‐being of children across the world. The project aims to integrate information from multiple child growth studies to allow health professionals and policy makers to make informed decisions about interventions in lower and middle income countries. To achieve this goal, we must first understand the conditions that impact on the growth and development of children, and this requires sensible models for characterising different growth patterns. The contribution of this paper is to provide a quantitative comparison of the predictive abilities of various statistical growth modelling techniques based on a novel leave‐one‐out validation approach. The majority of existing studies have used raw growth data for modelling, but we show that fitting models to standardised data provide more accurate estimation and prediction. Our work is illustrated with an example from a study into child development in a middle income country in South America.

Item Type:Articles
Additional Information:This study was supported by the Bill & Melinda Gates Foundation.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Anderson, Dr Craig
Authors: Anderson, C., Hafen, R., Sofrygin, O., and Ryan, L.
Subjects:H Social Sciences > HA Statistics
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Statistics in Medicine
Publisher:Wiley
ISSN:0277-6715
ISSN (Online):1097-0258
Published Online:09 August 2018
Copyright Holders:Copyright © 2018 The Authors
First Published:First published in Statistics in Medicine 38(18):3555-3570
Publisher Policy:Reproduced under a Creative Commons License

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