New analytic approaches in auxology

Baker, Jack

doi:10.1038/pr.2013.65

Cited by 1 publication

(1 citation statement)

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“…Using best modeling approach that incorporates both individual and population level variation for data characterized by irregular growth paths over time is the primary interest for over decades. Understanding the ecology of human physical growth and development is more important than ever because recent condition of child health and wellbeing has association with later life [2].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Dynamic Linear Regression Analysis of Infant Growth by Weight

Debeko¹

2018

AJTAS

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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.

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Section: Introductionmentioning

confidence: 99%

Bayesian Dynamic Linear Regression Analysis of Infant Growth by Weight

Debeko¹

2018

AJTAS

View full text Add to dashboard Cite

show abstract

New analytic approaches in auxology

Cited by 1 publication

References 24 publications

Bayesian Dynamic Linear Regression Analysis of Infant Growth by Weight

Bayesian Dynamic Linear Regression Analysis of Infant Growth by Weight

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