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Comparing predictive abilities of longitudinal child growth models
Author(s) -
Anderson Craig,
Hafen Ryan,
Sofrygin Oleg,
Ryan Louise
Publication year - 2018
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7693
Subject(s) - estimation , raw data , computer science , psychological intervention , work (physics) , longitudinal data , child development , econometrics , data science , data mining , psychology , economics , developmental psychology , mechanical engineering , management , psychiatry , engineering , programming language
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.