FPCA-Based Method to Select Optimal Sampling Schedules that Capture Between-Subject Variability in Longitudinal Studies

• Published in: Biometrics Vol. 74; no. 1; pp. 229 - 238
• Main Authors: Wu, Meihua, Diez-Roux, Ana, Raghunathan, Trivellore E., Sánchez, Brisa N.
• Format: Journal Article
• Language: English
• Published: England Wiley-Blackwell 01.03.2018; Blackwell Publishing Ltd
• Subjects: Appointments and Schedules; BIOMETRIC METHODOLOGY; biometry; Computer Simulation; Correlation analysis; Cortisol; Critical components; experimental design; Female; Humans; Hydrocortisone - analysis; Longitudinal design; Longitudinal Studies; Male; Menstrual Cycle; Nonlinear model design; Observer Variation; Optimal design; Optimization; principal component analysis; Principal Component Analysis - methods; Principal components analysis; Progesterone; Progesterone - urine; Salivary Glands - chemistry; Sampling; Schedules; statistical models; Temporal pattern; Time Factors; variance; Nonlinear model design; Longitudinal design; Temporal pattern; Optimal design
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.12714

A critical component of longitudinal study design involves determining the sampling schedule. Criteria for optimal design often focus on accurate estimation of the mean profile, although capturing the between-subject variance of the longitudinal process is also important since variance patterns may be associated with covariates of interest or predict future outcomes. Existing design approaches have limited applicability when one wishes to optimize sampling schedules to capture between-individual variability. We propose an approach to derive optimal sampling schedules based on functional principal component analysis (FPCA), which separately characterizes the mean and the variability of longitudinal profiles and leads to a parsimonious representation of the temporal pattern of the variability. Simulation studies show that the new design approach performs equally well compared to an existing approach based on parametric mixed model (PMM) when a PMM is adequate for the data, and outperforms the PMM-based approach otherwise. We use the methods to design studies aiming to characterize daily salivary cortisol profiles and identify the optimal days within the menstrual cycle when urinary progesterone should be measured.