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Entropy
Volume 24
Issue 2
10.3390/e24020232
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Article

Associations between Longitudinal Gestational Weight Gain and Scalar Infant Birth Weight: A Bayesian Joint Modeling Approach

by
Matthew Pietrosanu
1,
Linglong Kong
1,
Yan Yuan
2,
Rhonda C. Bell
3,
Nicole Letourneau
4 and
Bei Jiang
1,*
1
Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada
2
School of Public Health, University of Alberta, Edmonton, AB T6G 1C9, Canada
3
Department of Agricultural, Food & Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada
4
Faculty of Nursing and Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
*
Author to whom correspondence should be addressed.
Entropy 2022, 24(2), 232; https://doi.org/10.3390/e24020232
Submission received: 16 December 2021 / Revised: 26 January 2022 / Accepted: 29 January 2022 / Published: 2 February 2022
(This article belongs to the Special Issue Big Data Analytics and Information Science for Business and Biomedical Applications II)
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Abstract

Despite the importance of maternal gestational weight gain, it is not yet conclusively understood how weight gain during different stages of pregnancy influences health outcomes for either mother or child. We partially attribute this to differences in and the validity of statistical methods for the analysis of longitudinal and scalar outcome data. In this paper, we propose a Bayesian joint regression model that estimates and uses trajectory parameters as predictors of a scalar response. Our model remedies notable issues with traditional linear regression approaches found in the clinical literature. In particular, our methodology accommodates nonprospective designs by correcting for bias in self-reported prestudy measures; truly accommodates sparse longitudinal observations and short-term variation without data aggregation or precomputation; and is more robust to the choice of model changepoints. We demonstrate these advantages through a real-world application to the Alberta Pregnancy Outcomes and Nutrition (APrON) dataset and a comparison to a linear regression approach from the clinical literature. Our methods extend naturally to other maternal and infant outcomes as well as to areas of research that employ similarly structured data.
Keywords: Bayesian modeling; functional regression; gestational weight; infant birth weight; joint modeling; longitudinal data; maternal weight gain Bayesian modeling; functional regression; gestational weight; infant birth weight; joint modeling; longitudinal data; maternal weight gain

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MDPI and ACS Style

Pietrosanu, M.; Kong, L.; Yuan, Y.; Bell, R.C.; Letourneau, N.; Jiang, B. Associations between Longitudinal Gestational Weight Gain and Scalar Infant Birth Weight: A Bayesian Joint Modeling Approach. Entropy 2022, 24, 232. https://doi.org/10.3390/e24020232

AMA Style

Pietrosanu M, Kong L, Yuan Y, Bell RC, Letourneau N, Jiang B. Associations between Longitudinal Gestational Weight Gain and Scalar Infant Birth Weight: A Bayesian Joint Modeling Approach. Entropy. 2022; 24(2):232. https://doi.org/10.3390/e24020232

Chicago/Turabian Style

Pietrosanu, Matthew, Linglong Kong, Yan Yuan, Rhonda C. Bell, Nicole Letourneau, and Bei Jiang. 2022. "Associations between Longitudinal Gestational Weight Gain and Scalar Infant Birth Weight: A Bayesian Joint Modeling Approach" Entropy 24, no. 2: 232. https://doi.org/10.3390/e24020232

APA Style

Pietrosanu, M., Kong, L., Yuan, Y., Bell, R. C., Letourneau, N., & Jiang, B. (2022). Associations between Longitudinal Gestational Weight Gain and Scalar Infant Birth Weight: A Bayesian Joint Modeling Approach. Entropy, 24(2), 232. https://doi.org/10.3390/e24020232

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