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Activity Number: 353
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #311885
Title: A Multi-State Model for Dementia with Backward Transitions and Missing Covariates
Author(s): Wenjie Lou*+
Companies:
Keywords: Continuous-time multi-state model ; time homogenous ; backward transition ; missing covariate
Abstract:

Continuous-time multi-state models are often used to model the flow of elderly subjects from cognitively normal to Mild Cognitive Impairment (MCI) and/or dementia with death as a competing risk. Backward transitions and missing covariates are two major issues encountered in longitudinal datasets routinely collected by most Alzheimer's disease centers. We construct a time homogenous Markov model which allows backward cognitive transitions, for example, from MCI to normal cognition. Cox type proportional hazards are used to incorporate risk factors into the model. We treat missing covariates as random effects. Marginal probability is used in building the likelihood function for the missing covariates case. Simulation studies will be presented that explain when the missing pattern of the covariates does not affect the accuracy of the parameter estimates. We apply this model to data from the SMART study, a longitudinal cohort of 5,404 subjects each cognitively intact at baseline.


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