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dc.contributor.authorSimpkin, Andrew J.
dc.contributor.authorDurban, Maria
dc.contributor.authorLawlor, Debbie A.
dc.contributor.authorMacDonald-Wallis, Corrie
dc.contributor.authorMay, Margaret T.
dc.contributor.authorMetcalfe, Chris
dc.contributor.authorTilling, Kate
dc.date.accessioned2018-09-20T16:24:48Z
dc.date.available2018-09-20T16:24:48Z
dc.date.issued2018-05-20
dc.identifier.citationSimpkin, Andrew J. Durban, Maria; Lawlor, Debbie A.; MacDonald-Wallis, Corrie; May, Margaret T.; Metcalfe, Chris; Tilling, Kate (2018). Derivative estimation for longitudinal data analysis: examining features of blood pressure measured repeatedly during pregnancy. Statistics in Medicine 37 (19), 2836-2854
dc.identifier.issn0277-6715
dc.identifier.urihttp://hdl.handle.net/10379/13922
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 approachespolynomial mixed models and spline mixed models. We compare their performance with an established methodprincipal 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.publisherWiley
dc.relation.ispartofStatistics in Medicine
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/
dc.subjectalspac
dc.subjectderivative estimation
dc.subjectfunctional data analysis
dc.subjectlongitudinal data analysis
dc.subjectpenalized splines
dc.subjectcorrected confidence bands
dc.subjectsparse functional data
dc.subjectchange-point
dc.subjectnonparametric regression
dc.subjectfractional polynomials
dc.subjectprincipal components
dc.subjectspline models
dc.subjectcurves
dc.subjectdynamics
dc.subjectpatterns
dc.titleDerivative estimation for longitudinal data analysis: examining features of blood pressure measured repeatedly during pregnancy
dc.typeArticle
dc.identifier.doi10.1002/sim.7694
dc.local.publishedsourcehttps://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.7694
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