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Sökning: onr:"swepub:oai:DiVA.org:umu-155680" > Methods for longitu...

Methods for longitudinal brain imaging studies with dropout

Gorbach, Tetiana, 1991- (författare)
Umeå universitet,Statistik,Stat4Reg
de Luna, Xavier, Professor (preses)
Umeå universitet,Statistik
Lundquist, Anders, PhD, 1978- (preses)
Umeå universitet,Statistik
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Lindquist, Martin A., Professor (opponent)
Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health at Johns Hopkins University, Baltimore, USA
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 (creator_code:org_t)
ISBN 9789178550111
Umeå : Umeå universitet, 2019
Engelska 20 s.
Serie: Statistical studies, 1100-8989 ; 54
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)
Abstract Ämnesord
Stäng  
  • One of the challenges in aging research is to understand the brain mechanisms that underlie cognitive development in older adults. Such aging processes are investigated in longitudinal studies, where the within-individual changes over time are observed. However, several methodological issues exist in longitudinal analyses.  One of them is loss of participants to follow-up, which occurs when individuals drop out from the study. Such dropout should be taken into account for valid conclusions from longitudinal investigations, and this is the focus of this thesis. The developed methods are used to explore brain aging and its relation to cognition within the Betula longitudinal study of aging.Papers I and II consider the association between changes in brain structure and cognition. In the first paper, regression analysis is used to establish the statistical significance of brain-cognition associations while accounting for dropout. Paper II develops interval estimators directly for an association as measured by partial correlation, when some data are missing. The estimators of Paper II may be used in longitudinal as well as cross-sectional studies and are not limited to brain imaging. Papers III and IV study functional brain connectivity, which is the statistical dependency between the functions of distinct brain regions. Typically, only brain regions with associations stronger than a predefined threshold are considered connected. However, the threshold is often arbitrarily set and does not reflect the individual differences in the overall connectivity patterns.  Paper III proposes a mixture model for brain connectivity without explicit thresholding of associations and suggests an alternative connectivity measure. Paper IV extends the mixture modeling of Paper III to a longitudinal setting with dropout and investigates the impact of ignoring the dropout mechanism on the quality of the inferences made on longitudinal connectivity changes.

Ämnesord

NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

Nyckelord

Missing data
nonignorable dropout
sensitivity analysis
uncertainty intervals
pattern-mixture models
aging
cognition
MRI
brain structure
resting-state functional connectivity
Statistics
statistik

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