The most common anthropometric measurements used to assess physical growth patterns of infant from birth to one year period are body weight and length. Weight gain pattern is dynamic that could not be easily understood. The main objective of this study is to model the biological growth of infants by weight during the first year of their lives using the Bayesian hierarchical and dynamic linear regression model. The data used in this study was from a cohort study for infants born alive and followed from birth to one year period with six visits at Adare General Hospital. There has been a sample of 126 infants under follow-up from birth to 12 months old at Adare General Hospital, Hawassa Ethiopia. A total of 756 weight observations were collected from the following-up of the infants during the one year period. The Bayesian hierarchical and dynamic linear regression model was used to explore weight gain of infants incorporating individual and population level variations observed over time. The mean weight growth of the infants is found to be linearly increasing while variation was declining over the age. Rate of weight change of the infants had two optimum points that might represent inflection points of the growth at around six and eight months. Posterior distributions of the intercept and slope parameters were found to have normal distributions, from which important inferences about the infant’s growth can be derived. The Bayesian hierarchical and dynamic linear model can explain and capable to handle the weight growth patterns of the infants over the short period of time.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 7, Issue 3) |
| DOI | 10.11648/j.ajtas.20180703.12 |
| Page(s) | 102-111 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2018. Published by Science Publishing Group |
Infants, Weight, Bayesian Hierarchical, Dynamic Linear Model, Gibbs Sampler
| [1] | Spyrides, M. H. C., Struchiner, CJ, Tereza, M., Barbosa, S. and Kac G., (2008). Effect of Predominant Breastfeeding Duration on Infant Growth: A Prospective Study using Nonlinear Mixed Effect Models. J Pediatr (Rio J), 84(3) pp. 237-243. |
| [2] | Baker J., (2013). New Analytic Approaches in Auxology, Pediatric Research, 74(1) pp. 2-4. |
| [3] | Driver C. C. and Voelkle M. C., (2016). Hierarchical Bayesian Continuous Time Dynamic Modeling; Unpublished Draft manuscript, pp. 1-19. |
| [4] | Oravecz Z. and Muth C., (2017). Fitting Growth Curve Models in the Bayesian Framework. Springer link, pp. 1-21. |
| [5] | Chirwa E. D., Griffiths P. L., Maleta K., Norris S. A., Cameron N., (2014). Multilevel Modeling of Longit udinal Child Growth Data from Birth to Twenty Cohorts: A Comparison of Growth Models. Annals of Human Biology. Journal of the Society for the Study of Human Biology, 41(2) pp. 168-179. |
| [6] | Grimm K. J., Ram N., and Hamagami F., (2011): Nonlinear Growth Curves in Developmental Research. Child Development, 82 (5) pp. 1357–1371. |
| [7] | Johnson W., Balakrishna N., and Griffiths P. L., (2013). Modeling Physical Growth using Mixed Effects Models. Am J PhysAnthropol, 150 (1): 58–67. |
| [8] | Stirnemann J. J, Samson A., and Thalabard J. C., (2011). Individual Predictions based on Nonlinear Mixed Modeling: Application to Prenatal Twin Growth. Statistics in Medicine, 00 pp. 1–15. |
| [9] | Wang J., Luo S., and Li L., (2016). Dynamic Prediction for Multiple Repeated Measures and Event Time Data: An Application to Parkinson’s Disease. Cornell University Library. |
| [10] | Gliozzi A. S., Guiot C., Delsanto P. P. and Iordach D. A., (2012). A Novel Approach to the Analysis of Human Growth. Theoretical Biology and Medical Modelling, 9 pp 1-17. |
| [11] | Richard S. A., Benjamin J. J., and McCormick C. W., (2014). Modeling Environmental Influences on Child Growth in the MALED Cohort Study: Opportunities and Challenges. Clinical Infectious Diseases, 59 (4) pp. 255–60. |
| [12] | Cameron N., (2012): Human Growth Curve Canalization, and Catch-up Growth. In: Cameron, N. Editor. Human Growth and Development. pp. 1-24. |
| [13] | Brown W., and Leckie G., (2014). Modelling Longitudinal Data using the Stat-JR package. National Center for Research Methods (NCRM). Centre for Multilevel Modelling, University of Bristol. |
| [14] | Tenan S., (2012). Hierarchical Bayesian Modeling and Application in Animal Population Ecology. Unpublished PhD Thesis. University of Pavia. |
| [15] | Petris, G., Petrone, S., Compagnoli, P. (2007). Dynamic Linear Models with R. Monography. Springer |
| [16] | Huang Y., Liu D., and Wu H., (2006). Hierarchical Bayesian Methods for Estimation of Parameter in the Longitudinal HIV Dynamic Systems. Biometrics, 62 (2) pp. 413-423. |
| [17] | Gelman A., Carlin J. B., Stern H. S., and Rubin D. B., (2013): Bayesian Data Analysis (3rded). Boca Raton, F L.: Chapman & Hall/CRC. |
| [18] | Kruschke J., (2015). Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan. Academic Press, Elsevier. |
| [19] | McElreath R.., (2016). Statistical Rethinking: A Bayesian Course with Examples in R and Stan. A Chapman & Hall book Volume 122 of Chapman & Hall/CRC texts in statistical science series Volume 122 of Texts in statistical science, CRC Press/Taylor & Francis Group. |
| [20] | Gamerman D., and Loopes H. F., (2006). Markov Chain Monte Carlo Stochastic Simulation for Bayesian Inference (2ndEdition). Chapman and Hall/CRC. |
| [21] | Souza A. P., (1999). Approximate Methods in Bayesian Dynamic Hierarchical Models. Unpublished PhD Thesis. |
| [22] | Carlin B. C. and Louis T. A., (1996). Bayes and Empirical Bayes Methods for Data Analysis. Chapman& Hall, London. |
| [23] | Teshome D., Telahun T., Solomon D., and Abdulhamid I., (2006). A Study on Birth Weight in a Teaching Referral Hospital, Gonder, Ethiopia. Cent Afr J Med, 52(1-2) pp. 8-11. |
| [24] | Bhutta Z. A., Das J. K., Rizvi A., Gaffey M. F., Walker N., Horton S., Webb P., Anna Lartey, and Black R. E., (2013). Evidence-Based Interventions for Improvement of Maternal and Child Nutrition: what can be done and at what cost? The Lancet Nutrition Interventions Review Group, and the Maternal and Child Nutrition Study Group. Maternal and Child Nutrition 2, pp. 1-26. |
APA Style
Dereje Danbe Debeko, Ayele Taye Goshu. (2018). Bayesian Dynamic Linear Regression Analysis of Infant Growth by Weight. American Journal of Theoretical and Applied Statistics, 7(3), 102-111. https://doi.org/10.11648/j.ajtas.20180703.12
ACS Style
Dereje Danbe Debeko; Ayele Taye Goshu. Bayesian Dynamic Linear Regression Analysis of Infant Growth by Weight. Am. J. Theor. Appl. Stat. 2018, 7(3), 102-111. doi: 10.11648/j.ajtas.20180703.12
AMA Style
Dereje Danbe Debeko, Ayele Taye Goshu. Bayesian Dynamic Linear Regression Analysis of Infant Growth by Weight. Am J Theor Appl Stat. 2018;7(3):102-111. doi: 10.11648/j.ajtas.20180703.12
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ajtas.20180703.12,
author = {Dereje Danbe Debeko and Ayele Taye Goshu},
title = {Bayesian Dynamic Linear Regression Analysis of Infant Growth by Weight},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {7},
number = {3},
pages = {102-111},
doi = {10.11648/j.ajtas.20180703.12},
url = {https://doi.org/10.11648/j.ajtas.20180703.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20180703.12},
abstract = {The most common anthropometric measurements used to assess physical growth patterns of infant from birth to one year period are body weight and length. Weight gain pattern is dynamic that could not be easily understood. The main objective of this study is to model the biological growth of infants by weight during the first year of their lives using the Bayesian hierarchical and dynamic linear regression model. The data used in this study was from a cohort study for infants born alive and followed from birth to one year period with six visits at Adare General Hospital. There has been a sample of 126 infants under follow-up from birth to 12 months old at Adare General Hospital, Hawassa Ethiopia. A total of 756 weight observations were collected from the following-up of the infants during the one year period. The Bayesian hierarchical and dynamic linear regression model was used to explore weight gain of infants incorporating individual and population level variations observed over time. The mean weight growth of the infants is found to be linearly increasing while variation was declining over the age. Rate of weight change of the infants had two optimum points that might represent inflection points of the growth at around six and eight months. Posterior distributions of the intercept and slope parameters were found to have normal distributions, from which important inferences about the infant’s growth can be derived. The Bayesian hierarchical and dynamic linear model can explain and capable to handle the weight growth patterns of the infants over the short period of time.},
year = {2018}
}
TY - JOUR T1 - Bayesian Dynamic Linear Regression Analysis of Infant Growth by Weight AU - Dereje Danbe Debeko AU - Ayele Taye Goshu Y1 - 2018/04/02 PY - 2018 N1 - https://doi.org/10.11648/j.ajtas.20180703.12 DO - 10.11648/j.ajtas.20180703.12 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 102 EP - 111 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20180703.12 AB - The most common anthropometric measurements used to assess physical growth patterns of infant from birth to one year period are body weight and length. Weight gain pattern is dynamic that could not be easily understood. The main objective of this study is to model the biological growth of infants by weight during the first year of their lives using the Bayesian hierarchical and dynamic linear regression model. The data used in this study was from a cohort study for infants born alive and followed from birth to one year period with six visits at Adare General Hospital. There has been a sample of 126 infants under follow-up from birth to 12 months old at Adare General Hospital, Hawassa Ethiopia. A total of 756 weight observations were collected from the following-up of the infants during the one year period. The Bayesian hierarchical and dynamic linear regression model was used to explore weight gain of infants incorporating individual and population level variations observed over time. The mean weight growth of the infants is found to be linearly increasing while variation was declining over the age. Rate of weight change of the infants had two optimum points that might represent inflection points of the growth at around six and eight months. Posterior distributions of the intercept and slope parameters were found to have normal distributions, from which important inferences about the infant’s growth can be derived. The Bayesian hierarchical and dynamic linear model can explain and capable to handle the weight growth patterns of the infants over the short period of time. VL - 7 IS - 3 ER -
Email: