Attrition in Studies of Cognitive Aging
2013 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Bortfall i studier av kognitivt åldrande (Swedish)
Longitudinal studies of cognition are preferred to cross-sectional stud- ies, since they offer a direct assessment of age-related cognitive change (within-person change). Statistical methods for analyzing age-related change are widely available. There are, however, a number of challenges accompanying such analyzes, including cohort differences, ceiling- and floor effects, and attrition. These difficulties challenge the analyst and puts stringent requirements on the statistical method being used.
The objective of Paper I is to develop a classifying method to study discrepancies in age-related cognitive change. The method needs to take into account the complex issues accompanying studies of cognitive aging, and specifically work out issues related to attrition. In a second step, we aim to identify predictors explaining stability or decline in cognitive performance in relation to demographic, life-style, health-related, and genetic factors.
In the second paper, which is a continuation of Paper I, we investigate brain characteristics, structural and functional, that differ between suc- cessful aging elderly and elderly with an average cognitive performance over 15-20 years.
In Paper III we develop a Bayesian model to estimate the causal effect of living arrangement (living alone versus living with someone) on cog- nitive decline. The model must balance confounding variables between the two living arrangement groups as well as account for non-ignorable attrition. This is achieved by combining propensity score matching with a pattern mixture model for longitudinal data.
In paper IV, the objective is to adapt and implement available impu- tation methods to longitudinal fMRI data, where some subjects are lost to follow-up. We apply these missing data methods to a real dataset, and evaluate these methods in a simulation study.
Place, publisher, year, edition, pages
Umeå: Umeå universitet , 2013. , 21 p.
Statistical studies, ISSN 1100-8989 ; 47
Attrition, missing data, age-related cognitive change, non- ignorable dropout, monotone missing pattern, mixture models, pattern- mixture models, imputation
Other Social Sciences not elsewhere specified
Research subject Statistics
IdentifiersURN: urn:nbn:se:umu:diva-82514ISBN: 978-91-7459-760-8OAI: oai:DiVA.org:umu-82514DiVA: diva2:661682
2013-11-29, Humanisthuset, Hörsal F, Umeå universitet, Umeå, 10:15 (English)
Villani, Mattias, Professor
de Luna, Xavier, ProfessorNyberg, Lars, ProfessorLundquist, Anders, PhD
List of papers