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Using data from multiple studies to develop a child growth correlation matrix
Author(s) -
Anderson Craig,
Xiao Luo,
Checkley William
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.7696
Subject(s) - pooling , construct (python library) , univariate , computer science , correlation , raw data , longitudinal data , matrix (chemical analysis) , data set , set (abstract data type) , data mining , data matrix , statistics , econometrics , multivariate statistics , mathematics , artificial intelligence , machine learning , biology , geometry , gene , composite material , programming language , phylogenetic tree , clade , biochemistry , materials science
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.