Derivative estimation for longitudinal data analysis: examining features of blood pressure measured repeatedly during pregnancy

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dc.contributor.authorSimpkin, A.J.
dc.contributor.authorDurban, M.
dc.contributor.authorLawlor, D.A.
dc.contributor.authorMacDonald-Wallis, C.
dc.contributor.authorMay, M.T.
dc.contributor.authorMetcalfe, C.
dc.contributor.authorTilling, K.
dc.date.issued2018
dc.description.abstractEstimating velocity and acceleration trajectories allows novel inferences in the field of longitudinal data analysis, such as estimating change regions rather than change points, and testing group effects on nonlinear change in an outcome (ie, a nonlinear interaction). In this article, we develop derivative estimation for 2 standard approaches-polynomial mixed models and spline mixed models. We compare their performance with an established method-principal component analysis through conditional expectation through a simulation study. We then apply the methods to repeated blood pressure (BP) measurements in a UK cohort of pregnant women, where the goals of analysis are to (i) identify and estimate regions of BP change for each individual and (ii) investigate the association between parity and BP change at the population level. The penalized spline mixed model had the lowest bias in our simulation study, and we identified evidence for BP change regions in over 75% of pregnant women. Using mean velocity difference revealed differences in BP change between women in their first pregnancy compared with those who had at least 1 previous pregnancy. We recommend the use of penalized spline mixed models for derivative estimation in longitudinal data analysis.
dc.description.statementofresponsibilityAndrew J. Simpkin, Maria Durban, Debbie A. Lawlor, Corrie MacDonald‐Wallis, Margaret T. May, Chris Metcalfe, Kate Tilling
dc.identifier.citationStatistics in Medicine, 2018; 37(19):2836-2854
dc.identifier.doi10.1002/sim.7694
dc.identifier.issn0277-6715
dc.identifier.issn1097-0258
dc.identifier.orcidLawlor, D.A. [0000-0002-6793-2262]
dc.identifier.urihttp://hdl.handle.net/2440/120024
dc.language.isoen
dc.publisherWiley
dc.rightsŠ 2018 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
dc.source.urihttps://doi.org/10.1002/sim.7694
dc.subjectALSPAC
dc.subjectderivative estimation
dc.subjectfunctional data analysis
dc.subjectlongitudinal data analysis
dc.subjectpenalized splines
dc.titleDerivative estimation for longitudinal data analysis: examining features of blood pressure measured repeatedly during pregnancy
dc.typeJournal article
pubs.publication-statusPublished

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