Using data from multiple studies to develop a child growth correlation matrix

Publication Type:
Journal Article
Citation:
Statistics in Medicine, 2018
Issue Date:
2018-01-01
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
Anderson_et_al-2017-Statistics_in_Medicine.pdfPublished Version36.96 MB
Adobe PDF
© 2018 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd In many countries, the monitoring of child growth does not occur in a regular manner, and instead, wemay 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 childrenmay miss out on essential health interventions. The contribution of this paper is to provide a framework for pooling togethermultiple 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 fromthe Bill and MelindaGates 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.
Please use this identifier to cite or link to this item: