2021
DOI: 10.1097/hjh.0000000000002995
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Maternal blood pressure trajectories and associations with gestational age at birth: a functional data analytic approach

Abstract: Background: Research has revealed group-level differences in maternal blood pressure trajectories across pregnancy. These trajectories are typically constructed using clinical blood pressure data and multivariate statistical methods that are prone to bias and ignore the functional, dynamic process underlying a single blood pressure observation. The aim of this study was to use functional data analysis to explore blood pressure variation across pregnancy, and multivariate methods to examine whether trajectories… Show more

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Cited by 3 publications
(2 citation statements)
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References 40 publications
(86 reference statements)
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“…Similar to results from our study, prenatal anxiety and depressive disorders have also been shown to significantly increase a person’s risk for a HDP diagnosis, and in some instances result in a shorter gestational period (37). Results from a prospective Mexican cohort showed that anxiety, sleep or social dysfunction, and acute somatic symptoms at 20 weeks of gestation were associated with a higher risk of developing HDP (38).…”
Section: Discussionsupporting
confidence: 89%
“…Similar to results from our study, prenatal anxiety and depressive disorders have also been shown to significantly increase a person’s risk for a HDP diagnosis, and in some instances result in a shorter gestational period (37). Results from a prospective Mexican cohort showed that anxiety, sleep or social dysfunction, and acute somatic symptoms at 20 weeks of gestation were associated with a higher risk of developing HDP (38).…”
Section: Discussionsupporting
confidence: 89%
“…Functional data analysis (FDA) is the branch of statistics that deals with observations varying over a continuous parameter, such as curves, surfaces, and other types of functions (Cuevas 2014;Wang, Chiou, and Müller 2016). These types of data appear in many different fields, such as biology (Cremona, Xu, Makova, Reimherr, Chiaromonte, and Madrigal 2019), demographics (Hyndman and Shahid Ullah 2007), economics (Frois Caldeira, Gupta, Suleman, and Torrent 2020), energy security (Gong, Wang, and Lin 2021), genomics (Leng and Müller 2006;Chen, Cremona, Qi, Mitra, Chiaromonte, and Makova 2020), medicine (Sørensen, Goldsmith, and Sangalli 2013;Ferrando, Ventura-Campos, and Epifanio 2020;Horsley, Ramsay, Ditto, and Da Costa 2021), meteorology (Beyaztas and Yaseen 2019), oceanography (Assunção et al 2020), traffic control (Wagner-Muns, Guardiola, Samaranayke, and Kayani 2018;Hu, Yuan, Zhu, Yang, and Xie 2019), and other areas of application (Ullah and Finch 2013). The functional nature of these data and, in particular, their continuous structure, entails important differences with respect to the classical multivariate statistics.…”
Section: Introductionmentioning
confidence: 99%