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Modelling human height and weight: a Bayesian approach towards model comparison

European Journal of Clinical Nutrition volume 70, pages 656–661 (2016)Cite this article

Subjects

Abstract

Background/Objectives:

Given the availability of large longitudinal data sets on human height and weight, different modelling approaches are at hand to access quantities of interest relating to important diagnostic aims.

Subjects/Methods:

Statistical modelling frameworks for longitudinal data on human height and weight have to consider the issues of individual heterogeneity and time dependence to provide an accurate statistical characterisation. Further, missing values inevitably occurring within longitudinal data sets have to be addressed adequately to allow for valid inference. The Bayesian framework is illustrated to facilitate stringent comparison of available non-nested model frameworks addressing these issues using simulated and empirical data sets.

Results:

Comparing random-effects and fixed-effects modelling approaches with the Preece–Baines (PB) model reveals that, for simulated data, the Bayesian approach towards model comparison is effective in discriminating between different model specifications. With regard to analysis of 14 longitudinal data sets, the implicit trade-off between model fit, that is, description of the data, and a parsimonious parameterisation favouring prediction is often best addressed via the PB model.

Conclusions:

The Bayesian approach is illustrated to allow for effective comparison in case model specifications for longitudinal data are not linked directly via parametric restrictions.

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References

  1. WHO WHO Child Growth Standards: Length/Height-For-Age, Weight-For-Age, Weight-For-Length, Weight-For-Height and Body Mass Index-For-Age: Methods and Development. World Health Organization: Geneva, Switzerland, 2006.

  2. Preece MA, Baines MJ . A new family of mathematical models describing the human growth curve. Ann Hum Biol 1978; 5: 1–24.

    Article  CAS  Google Scholar 

  3. Jenss RM, Bayley N . A mathematical method for studying growth in children. Hum Biol 1937; 9: 556–563.

    Google Scholar 

  4. Cole TJ, Green PJ . Smoothing reference centile curves: the LMS method and penalized likelihood. Stat Med 1992; 11: 1305–1319.

    Article  CAS  Google Scholar 

  5. Cole TJ, Donaldson, Malcolm DC, Ben-Shlomo Y . SITAR—a useful instrument for growth curve analysis. Int J Epidemiol 2010; 39: 1558–1566.

    Article  Google Scholar 

  6. Kass RE, Raftery AE . Bayes factors. J Am Stat Assoc 1995; 90: 773–795.

    Article  Google Scholar 

  7. Aßmann C, Hermanussen M . Modeling determinants of growth: evidence for a community-based target in height? Pediatr Res 2013; 74: 88–95.

    Article  Google Scholar 

  8. Tanner MA, Wong WH . The calculation of posterior distributions by data augmentation. J Am Stat Assoc 1987; 82: 528–540.

    Article  Google Scholar 

  9. Casella G, George EI . Explaining the Gibbs sampler. Am Stat 1992; 46: 167–174.

    Google Scholar 

  10. Chib S . Marginal likelihood from the Gibbs output. J Am Stat Assoc 1995; 90: 1313–1321.

    Article  Google Scholar 

  11. Chib S, Jeliazkov I . Marginal likelihood from the Metropolis–Hastings output. J Am Stat Assoc 2001; 96: 270–281.

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Statistics and Econometrics, Otto-Friedrich-Universität, Bamberg, Germany

    M Preising

  2. Department of Anatomy, Histology and Anthropology, Faculty of Medicine, Vilnius University, Vilnius, Lithuania

    A Suchomlinov & J Tutkuviene

  3. Leibniz Institute for Educational Trajectories, Bamberg, Germany

    C Aßmann

Authors
  1. M Preising
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  2. A Suchomlinov
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  3. J Tutkuviene
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  4. C Aßmann
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Corresponding author

Correspondence to M Preising.

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Preising, M., Suchomlinov, A., Tutkuviene, J. et al. Modelling human height and weight: a Bayesian approach towards model comparison. Eur J Clin Nutr 70, 656–661 (2016). https://doi.org/10.1038/ejcn.2016.23

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  • DOI: https://doi.org/10.1038/ejcn.2016.23

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