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Causal inference with longitudinal outcomes and non-ignorable drop-out: Estimating the effect of living alone on cognitive decline
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
Umeå University, Faculty of Social Sciences, Department of Statistics.
University of Texas at Austin.
Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI). Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology. Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB), Physiology.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

We develop a model to estimate the causal effect of living arrangement (living alone versus living with someone) on cognitive decline based on a 15-year prospective cohort study, where episodic memory function is measured every 5 years. One key feature of the model is the combination of propensity score matching to balance confounding variables between the two living arrangement groups—to reduce bias due to unbalanced covariates at baseline, with a pattern–mixture model for longitudinal data—to deal with non-ignorable dropout. A fully Bayesian approach allows us to convey the uncertainty in the estimation of the propensity score and subsequent matching in the inference of the causal effect of interest. The analysis conducted adds to previous studies in the literature concerning the protective effect of living with someone, by proposing a modelling approach treating living arrangement as an exposure.

Keyword [en]
Aging;Bayesian inference;Episodic memory;Non-ignorable missingness;Pattern–mixture model;Propensity score matching;Sensitivity
National Category
Social Sciences Interdisciplinary
Research subject
URN: urn:nbn:se:umu:diva-82511DOI: 10.1111/rssc.12110OAI: diva2:661662
Statistiska metoder för studier av kognitiv åldrande: kognitionstester och hjärnavbildning.
Swedish Research Council
Available from: 2013-11-04 Created: 2013-11-04 Last updated: 2015-06-25
In thesis
1. Attrition in Studies of Cognitive Aging
Open this publication in new window or tab >>Attrition in Studies of Cognitive Aging
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Bortfall i studier av kognitivt åldrande
Abstract [en]

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
National Category
Other Social Sciences not elsewhere specified
Research subject
urn:nbn:se:umu:diva-82514 (URN)978-91-7459-760-8 (ISBN)
Public defence
2013-11-29, Humanisthuset, Hörsal F, Umeå universitet, Umeå, 10:15 (English)
Available from: 2013-11-08 Created: 2013-11-04 Last updated: 2016-03-07Bibliographically approved

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Josefsson, Mariade Luna, XavierNyberg, Lars
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StatisticsDepartment of StatisticsUmeå Centre for Functional Brain Imaging (UFBI)Diagnostic RadiologyPhysiology
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