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Published, Version of Record (VoR)Open Access (License Unspecified), Open
Journal article
FPCA-based method to select optimal sampling schedules that capture between-subject variability in longitudinal studies
Biometrics, v 74(1)
Mar 2018
PMID: 28482120
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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Details
- Title
- FPCA-based method to select optimal sampling schedules that capture between-subject variability in longitudinal studies
- Creators
- Meihua Wu - Gilead Sciences, Inc., Foster City, California 94404, U.S.A.Ana Diez-Roux - Drexel UniversityTrivellore E Raghunathan - University of Michigan–Ann ArborBrisa N Sánchez - University of Michigan
- Publication Details
- Biometrics, v 74(1)
- Publisher
- Wiley
- Grant note
- N01HC95169 / NHLBI NIH HHS N01 HC095164 / NHLBI NIH HHS N01 HC095160 / NHLBI NIH HHS N01 HC095168 / NHLBI NIH HHS N01 HC095169 / NHLBI NIH HHS N01 HC095165 / NHLBI NIH HHS R21 DA024273 / NIDA NIH HHS N01 HC095162 / NHLBI NIH HHS N01HC95159 / NHLBI NIH HHS N01 HC095159 / NHLBI NIH HHS N01 HC095166 / NHLBI NIH HHS N01 HC095163 / NHLBI NIH HHS N01 HC095161 / NHLBI NIH HHS R01 HL101161 / NHLBI NIH HHS P30 ES017885 / NIEHS NIH HHS N01 HC095167 / NHLBI NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Urban Health Collaborative; Epidemiology and Biostatistics
- Web of Science ID
- WOS:000427562400024
- Scopus ID
- 2-s2.0-85019034309
- Other Identifier
- 991019169790304721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Biology
- Mathematical & Computational Biology
- Statistics & Probability