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Journal of the American Statistical Association (1)
Test (Madrid, Spain) (1)
The Journals of Gerontology Series A: Biological Sciences and Medical Sciences (1)
Daniels, Michael J. (2)
Carter, Christy S. (1)
Daniels, Michael (1)
Foster, Thomas C. (1)
Leeuwenburgh, Christiaan (1)
Liu, Xuefeng (1)
Marcus, Bess (1)
Wang, Chenguang (1)
Year of Publication
Discussion of “Missing Data Methods in Longitudinal Studies: A Review” by Ibrahim and Molenberghs
Test (Madrid, Spain)
Influence of Calorie Restriction on Measures of Age-Related Cognitive Decline: Role of Increased Physical Activity
Carter, Christy S.
Foster, Thomas C.
The Journals of Gerontology Series A: Biological Sciences and Medical Sciences
Controversy exists as to whether lifelong 40% calorie restriction (CR) enhances, has no effect on, or disrupts cognitive function during aging. Here, we report the effects of CR versus ad-lib feeding on cognitive function in male Brown Norway × Fisher344 rats across a range of ages (8–38 months), using two tasks that are differentially sensitive to age-related cognitive decline: object recognition and Morris water maze (MWM). All ages performed equally in object recognition, whereas, as a group, CR rats were impaired. In contrast, there was an age-related impairment in the MWM that was attenuated by CR as measured by time in proximity with and latency to reach the platform. Distance to the platform, a more sensitive measure, was not affected by CR. Finally, CR resulted in an overall increase in physical activity, one of several behavioral confounders to consider in the interpretation of cognitive outcomes in both tasks.
Morris water maze; Object recognition; Animal models of aging; Calorie restriction
Joint Models for the Association of Longitudinal Binary and Continuous Processes With Application to a Smoking Cessation Trial
Journal of the American Statistical Association
Joint models for the association of a longitudinal binary and a longitudinal continuous process are proposed for situations in which their association is of direct interest. The models are parameterized such that the dependence between the two processes is characterized by unconstrained regression coefficients. Bayesian variable selection techniques are used to parsimoniously model these coefficients. A Markov chain Monte Carlo (MCMC) sampling algorithm is developed for sampling from the posterior distribution, using data augmentation steps to handle missing data. Several technical issues are addressed to implement the MCMC algorithm efficiently. The models are motivated by, and are used for, the analysis of a smoking cessation clinical trial in which an important question of interest was the effect of the (exercise) treatment on the relationship between smoking cessation and weight gain.
Calibrated posterior predictive p-value; Data augmentation; Dependence; Joint models; Markov chain Monte Carlo; Parameter expansion; Stochastic search variable selection
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