TY  - JOUR
ID  - discovery10069726
SN  - 1471-2288
N1  - This work is licensed under a Creative Commons Attribution 4.0 International License. The images
or other third party material in this article are included in the article?s Creative Commons license,
unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license,
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license, visit http://creativecommons.org/licenses/by/4.0/
JF  - BMC Medical Research Methodology
AV  - public
N2  - BACKGROUND: Regression models are widely used to link serial measures of anthropometric size or changes in size to a later outcome. Different parameterisations of these models enable one to target different questions about the effect of growth, however, their interpretation can be challenging. Our objective was to formulate and classify several sets of parameterisations by their underlying growth pattern contrast, and to discuss their utility using an expository example. METHODS: We describe and classify five sets of model parameterisations in accordance with their underlying growth pattern contrast (conditional growth; being bigger v being smaller; becoming bigger and staying bigger; growing faster v being bigger; becoming and staying bigger versus being bigger). The contrasts are estimated by including different sets of repeated measures of size and changes in size in a regression model. We illustrate these models in the setting of linking infant growth (measured on 6 occasions: birth, 6 weeks, 3, 6, 12 and 24 months) in weight-for-height-for-age z-scores to later childhood overweight at 8y using complete cases from the Norwegian Childhood Growth study (n?=?900). RESULTS: In our expository example, conditional growth during all periods, becoming bigger in any interval and staying bigger through infancy, and being bigger from birth were all associated with higher odds of later overweight. The highest odds of later overweight occurred for individuals who experienced high conditional growth or became bigger in the 3 to 6 month period and stayed bigger, and those who were bigger from birth to 24 months. Comparisons between periods and between growth patterns require large sample sizes and need to consider how to scale associations to make comparisons fair; with respect to the latter, we show one approach. CONCLUSION: Studies interested in detrimental growth patterns may gain extra insight from reporting several sets of growth pattern contrasts, and hence an approach that incorporates several sets of model parameterisations. Co-efficients from these models require careful interpretation, taking account of the other variables that are conditioned on.
VL  - 16
Y1  - 2016/04/08/
A1  - Wills, AK
A1  - Strand, BH
A1  - Glavin, K
A1  - Silverwood, RJ
A1  - Hovengen, R
UR  - https://doi.org/10.1186/s12874-016-0143-1
TI  - Regression models for linking patterns of growth to a later outcome: infant growth and childhood overweight
KW  - Conditional regression modelling
KW  -  Growth
KW  -  Growth patterns
KW  -  Model parameterisations
KW  -  Age Factors
KW  -  Anthropometry
KW  -  Body Height
KW  -  Body Mass Index
KW  -  Body Weight
KW  -  Child
KW  -  Child Development
KW  -  Child
KW  -  Preschool
KW  -  Cohort Studies
KW  -  Confidence Intervals
KW  -  Female
KW  -  Humans
KW  -  Infant
KW  -  Infant
KW  -  Newborn
KW  -  Male
KW  -  Models
KW  -  Statistical
KW  -  Norway
KW  -  Odds Ratio
KW  -  Pediatric Obesity
KW  -  Regression Analysis
KW  -  Retrospective Studies
KW  -  Sex Factors
ER  -