ArticleStatistics in medicine2019
Comparing predictive abilities of longitudinal child growth models.
Article in Statistics in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Cluster analysis for longitudinal data and its application in the detection of adiposity trajectories.Statistical methods in medical research · 2026Article
- Modelling and Predicting Population-Level Growth With Individual-Level Information.Statistics in medicine · 2026Article
- Quantifying the fatal and non-fatal burden of disease associated with child growth failure, 2000-2023: a systematic analysis from the Global Burden of Disease Study 2023.The Lancet. Child & adolescent health · 2026Article
- Forecasting the Regional Demand for Medical Workers in Kazakhstan: The Functional Principal Component Analysis Approach.International journal of environmental research and public health · 2025Article
- Extending curve matching with flexible hyperparameter selection to predict response to long-acting PEGylated growth hormone treatment in growth hormone deficiency children: method development and validation.Journal of endocrinological investigation · 2025Article
- Advancing Pediatric Growth Assessment with Machine Learning: Overcoming Challenges in Early Diagnosis and Monitoring.Children (Basel, Switzerland) · 2025Article
- Postnatal growth in small vulnerable newborns: a longitudinal study of 2 million Brazilians using routine register-based linked data.The American journal of clinical nutrition · 2024Article
- A modified Michaelis-Menten equation estimates growth from birth to 3 years in healthy babies in the USA.BMC medical research methodology · 2024Article
- Prevalence, Incidence, and Reversal Pattern of Childhood Stunting From Birth to Age 2 Years in Ethiopia.JAMA network open · 2024Article
- A nationwide longitudinal investigation on the role of prenatal exposure to infectious diseases on the onset of chronic conditions in children and adolescents in Brazil.Wellcome open research · 2024Article
- Maternal and child nutrition programme of investigation within the 100 Million Brazilian Cohort: study protocol.BMJ open · 2023Article
- Functional principal component analysis for identifying the child growth pattern using longitudinal birth cohort data.BMC medical research methodology · 2022Article
- Neuroimaging brain growth charts: A road to mental health.Psychoradiology · 2021Article
- Neighbors-based prediction of physical function after total knee arthroplasty.Scientific reports · 2021Article
- Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth - a four-year prospective study.BMC pediatrics · 2020Article
- Comparison of growth models to describe growth from birth to 6 years in a Beninese cohort of children with repeated measurements.BMJ open · 2020Article
- Four papers on child growth modelling.Statistics in medicine · 2019Article
- Comparing predictive abilities of longitudinal child growth models.Statistics in medicine · 2019Article
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5 authors.
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No grant is acknowledged in the PubMed record.
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.
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