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Derivative estimation for longitudinal data analysis: Examining features of blood pressure measured repeatedly during pregnancy
Author(s) -
Simpkin Andrew J.,
Durban Maria,
Lawlor Debbie A.,
MacDonaldWallis Corrie,
May Margaret T.,
Metcalfe Chris,
Tilling Kate
Publication year - 2018
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7694
Subject(s) - spline (mechanical) , statistics , principal component analysis , mathematics , population , mixed model , random effects model , econometrics , computer science , medicine , meta analysis , environmental health , structural engineering , engineering
Estimating 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.