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Stat Methods Med Res. 2016 Oct;25(5):1854-1874. Epub 2013 Oct 9.

Linear spline multilevel models for summarising childhood growth trajectories: A guide to their application using examples from five birth cohorts.

Author information

1
MRC Integrative Epidemiology Unit at the University of Bristol, School of Social and Community Medicine, University of Bristol, UK laura.howe@bristol.ac.uk.
2
School of Social and Community Medicine, University of Bristol, UK.
3
Postgraduate Programme in Epidemiology, Federal University of Pelotas, Pelotas, Brazil.
4
Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK.
5
Department of Clinical Epidemiology, Predictive Medicine and Public Health, University of Porto Medical School, University of Porto Institute of Public Health, Porto, Portugal.
6
School of Social and Community Medicine, University of Bristol, UK National Institute for Health Research Bristol Nutrition Biomedical Research Unit, University of Bristol / University Hospitals Bristol NHS Foundation Trust, Bristol, UK.
7
Department of Pediatrics, Faculty of Medicine, McGill University, Montreal, Canada.
8
Belarusian Ministry of Health and Belarussian Maternal and Child Health Research Institute, Minsk, Belarus.
9
MRC Integrative Epidemiology Unit at the University of Bristol, School of Social and Community Medicine, University of Bristol, UK.

Abstract

Childhood growth is of interest in medical research concerned with determinants and consequences of variation from healthy growth and development. Linear spline multilevel modelling is a useful approach for deriving individual summary measures of growth, which overcomes several data issues (co-linearity of repeat measures, the requirement for all individuals to be measured at the same ages and bias due to missing data). Here, we outline the application of this methodology to model individual trajectories of length/height and weight, drawing on examples from five cohorts from different generations and different geographical regions with varying levels of economic development. We describe the unique features of the data within each cohort that have implications for the application of linear spline multilevel models, for example, differences in the density and inter-individual variation in measurement occasions, and multiple sources of measurement with varying measurement error. After providing example Stata syntax and a suggested workflow for the implementation of linear spline multilevel models, we conclude with a discussion of the advantages and disadvantages of the linear spline approach compared with other growth modelling methods such as fractional polynomials, more complex spline functions and other non-linear models.

KEYWORDS:

ALSPAC; Born in Bradford; Generation XXI; PROBIT; Pelotas; child; growth; height; longitudinal; multilevel models; spline; weight

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