Cross-sectional versus longitudinal designs for function estimation, with an application to cerebral cortex development

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Abstract

Motivated by studies of the development of the human cerebral cortex, we consider the estimation of a mean growth trajectory and the relative merits of cross-sectional and longitudinal data for that task. We define a class of relative efficiencies that compare function estimates in terms of aggregate variance of a parametric function estimate. These generalize the classical design effect for estimating a scalar with cross-sectional versus longitudinal data, and are shown to be bounded above by it in certain cases. Turning to nonparametric function estimation, we find that longitudinal fits may tend to have higher aggregate variance than cross-sectional ones, but that this may occur because the former have higher effective degrees of freedom reflecting greater sensitivity to subtle features of the estimand. These ideas are illustrated with cortical thickness data from a longitudinal neuroimaging study.

Original languageAmerican English
Pages (from-to)1895-1909
Number of pages15
JournalStatistics in Medicine
Volume37
Issue number11
DOIs
StatePublished - 20 May 2018

Keywords

  • accelerated longitudinal design
  • cortical thickness
  • design effect
  • effective degrees of freedom
  • penalized splines

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability

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