Derivative estimation for longitudinal data analysis: Examining features of blood pressure measured repeatedly during pregnancy
Estimating velocity and acceleration trajectories allow 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 (i.e. a nonlinear interaction). We develop derivative estimation in the context of spline mixed models. We compare their performance with Principal components Analysis through Conditional Expectation (PACE) in a simulation study. We also 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
Palabras clave / Keywords: derivative estimation functional data analysis longitudinal data analysis penalized splines
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