IntroductionCarefully designed longitudinal studies with repeated measures can deepen our understanding of how biological processes evolve and enhance our ability to identify predictors of change. Longitudinal study design, however, is complex since it involves: (i) the number of subjects; (ii) the number of samples per subject; and, in particular, (iii) the spacing between samples (i.e., sampling schedule), while meeting budgetary and logistical constraints. In a motivating example, investigators want to identify times during the day at which to collect salivary cortisol, a stress biomarker that follows a nonlinear profile (Figure a). In another example, it is of interest to identify a small number of days during the menstrual cycle at which to measure urinary progesterone (Figure a).(a) Scatterplot of salivary cortisol data described in Section ; (b) mean profile and (c)–(d) functional principal components for the variability structure in the simulations.Methods to determine the sampling schedule of repeated measures studies have received less attention than those for sample size and power calculations (e.g., Raudenbush and Liu, ; Retout et al., ; Stroud et al., ; Basagaña and Spiegelman, ). Available approaches include selecting optimal sampling schedules based on parametric nonlinear mixed models (PMM) (Fedorov and Hackl, ; Stroud et
Biometrics – Wiley
Published: Jan 1, 2018
Keywords: ; ; ;
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