ArticleStatistics in medicine2019
Using data from multiple studies to develop a child growth correlation matrix.
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 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quantifying child growth effects using height-age instead of height-for-age z-scores in a meta-analysis of small-quantity lipid-based nutrient supplement trials.Scientific reports · 2025Pooled it
- Stunting Reversal versus Regression to the Mean in Epidemiological Studies of Child Linear Growth in Low- and Middle-Income Countries.Advances in nutrition (Bethesda, Md.) · 2026Review
- An Introduction to Longitudinal Synthetic Cohorts for Studying the Life Course Drivers of Health Outcomes and Inequalities in Older Age.Current epidemiology reports · 2025Article
- Article
- Forecasting the Regional Demand for Medical Workers in Kazakhstan: The Functional Principal Component Analysis Approach.International journal of environmental research and public health · 2025Article
- Overcoming Data Gaps in Life Course Epidemiology by Matching Across Cohorts.Epidemiology (Cambridge, Mass.) · 2024Article
- Functional principal component analysis for identifying the child growth pattern using longitudinal birth cohort data.BMC medical research methodology · 2022Article
- Combining Longitudinal Data From Different Cohorts to Examine the Life-Course Trajectory.American journal of epidemiology · 2021Article
- Correlation models for monitoring fetal growth.Statistical methods in medical research · 2020Article
- Using data from multiple studies to develop a child growth correlation matrix.Statistics in medicine · 2019Article
- Four papers on child growth modelling.Statistics in medicine · 2019Article
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3 authors.
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Abstract
In many countries, the monitoring of child growth does not occur in a regular manner, and instead, we may have to rely on sporadic observations that are subject to substantial measurement error. In these countries, it can be difficult to identify patterns of poor growth, and faltering children may miss out on essential health interventions. The contribution of this paper is to provide a framework for pooling together multiple datasets, thus allowing us to overcome the issue of sparse data and provide improved estimates of growth. We use data from multiple longitudinal growth studies to construct a common correlation matrix that can be used in estimation and prediction of child growth. We propose a novel 2-stage approach: In stage 1, we construct a raw matrix via a set of univariate meta-analyses, and in stage 2, we smooth this raw matrix to obtain a more realistic correlation matrix. The methodology is illustrated using data from 16 child growth studies from the Bill and Melinda Gates Foundation's Healthy Birth Growth and Development knowledge integration project and identifies strong correlation for both height and weight between the ages of 4 and 12 years. We use a case study to provide an example of how this matrix can be used to help compute growth measures.
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