Commentary: Methods for calculating growth trajectories and constructing growth centiles
- PMID: 31298428
- PMCID: PMC6772074
- DOI: 10.1002/sim.8129
Commentary: Methods for calculating growth trajectories and constructing growth centiles
Abstract
This commentary rounds off a collection of papers focusing on statistical methods for analysing growth data. In two papers, Anderson and colleagues discuss growth trajectory models in early life, using data on height and weight from the HBGDki initiative, while two papers from Ohuma and Altman review methods for centile construction, with data from the INTERGROWTH-21st project used to provide worked examples of centiles for birthweight and fetal head circumference. Anderson et al focus on four growth trajectory models: quadratic Laird-Ware, SITAR, brokenstick, and FACE, where the latter two fit better than the former two applied to length data in individuals. On this basis, they recommend brokenstick and FACE for future work. However, they do not discuss the timescale on which the growth models assess growth faltering nor the relevance of this timescale to later health outcome. Models that best detect short-term fluctuations in growth (brokenstick and FACE) may not necessarily be best at predicting later outcome. It is premature to exclude the quadratic Laird-Ware or SITAR models, which give a parsimonious summary of growth in individuals over a longer timescale. Ohuma and Altman highlight the poor quality of reporting in fetal centile studies, and they provide recommendations for good practice. Their birthweight centiles example illustrates both the power of the GAMLSS software and its capacity for misuse. The longitudinal fetal head circumference centiles are biased such that 5% of infants are below the 3rd centile and 5% above the 97th .
© 2019 John Wiley & Sons, Ltd.
Comment in
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Response to Professor Cole's commentary: Methods for calculating growth trajectories and constructing growth centiles.Stat Med. 2019 Aug 30;38(19):3580-3583. doi: 10.1002/sim.8128. Stat Med. 2019. PMID: 31298429 No abstract available.
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Response to Professor Tim Cole's commentary: Methods for calculating growth trajectories and constructing growth centiles.Stat Med. 2019 Aug 30;38(19):3584-3585. doi: 10.1002/sim.8127. Stat Med. 2019. PMID: 31298430 No abstract available.
Comment on
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Using data from multiple studies to develop a child growth correlation matrix.Stat Med. 2019 Aug 30;38(19):3540-3554. doi: 10.1002/sim.7696. Epub 2018 Apr 26. Stat Med. 2019. PMID: 29700850 Free PMC article.
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Comparing predictive abilities of longitudinal child growth models.Stat Med. 2019 Aug 30;38(19):3555-3570. doi: 10.1002/sim.7693. Epub 2018 Aug 9. Stat Med. 2019. PMID: 30094965 Free PMC article.
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Design and other methodological considerations for the construction of human fetal and neonatal size and growth charts.Stat Med. 2019 Aug 30;38(19):3527-3539. doi: 10.1002/sim.8000. Epub 2018 Oct 23. Stat Med. 2019. PMID: 30352489 Free PMC article.
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Statistical methodology for constructing gestational age-related charts using cross-sectional and longitudinal data: The INTERGROWTH-21st project as a case study.Stat Med. 2019 Aug 30;38(19):3507-3526. doi: 10.1002/sim.8018. Epub 2018 Nov 28. Stat Med. 2019. PMID: 30488491 Free PMC article.
References
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- Jumbe NL, Murray JC, Kern S. Data sharing and inductive learning—toward healthy birth, growth, and development. N Engl J Med. 2016;374(25):2415‐2417. - PubMed
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- Laird NM, Ware JH. Random‐effects models for longitudinal data. Biometrics. 1982;38(4):963‐974. - PubMed
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- van Buuren S. brokenstick: broken stick model for irregular longitudinal data. 2017.
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- Durbán M, Harezlak J, Wand MP, Carroll RJ. Simple fitting of subject‐specific curves for longitudinal data. Statist Med. 2005;24(8):1153‐1167. - PubMed
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