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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.
Highlights
Maternal weight gain supports fetal growth and holds important health implications for both mother and child during and after pregnancy [1,2,3]
We present estimates for the effect of time-specific maternal weight gain on infant birth weight obtained under the proposed model in Section 3, and compare these estimates to those obtained using the linear regression approach described above [13]
We present our joint model for infant birth weight and longitudinal gestational weight gain
Summary
Maternal weight gain supports fetal growth and holds important health implications for both mother and child during and after pregnancy [1,2,3]. A growing amount of clinical literature further implicates maternal gestational weight gain outside of recommendations in adverse, longterm health outcomes for the child, including a heightened future risk of cardiovascular disease [6,7]. It is not yet conclusively understood how weight gain in different stages of pregnancy affects health outcomes for either mother or child. As an example central to this article, previous studies present conflicting conclusions on the effect of first- and second-trimester weight gain on infant birth weight [8,9,10,11,12] We attribute this in part to differences in and the validity of the statistical methods currently used to jointly analyze scalar outcomes and longitudinal data. Developments in methodology for analyzing how patterns in longitudinal data (e.g., gestational weight gain) influence scalar outcomes (e.g., infant birth weight) are both statistically and clinically relevant
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