Derivative estimation for longitudinal data analysis: Examining features of blood pressure measured repeatedly during pregnancy |
| |
| Authors: | Andrew J. Simpkin Maria Durban Debbie A. Lawlor Corrie MacDonald‐Wallis Margaret T. May Chris Metcalfe Kate Tilling |
| |
| Affiliation: | 1. MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK;2. Insight Centre for Data Analytics, NUI, Galway, Ireland;3. Department of Statistics, Universidad Carlos III de Madrid, Madrid, Spain;4. Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK;5. Centre for Exercise, Nutrition & Health Sciences, School for Policy Studies, University of Bristol, Bristol, UK |
| |
| Abstract: | 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. |
| |
| Keywords: | ALSPAC derivative estimation functional data analysis longitudinal data analysis penalized splines |
|
|
