Abstract #302010

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JSM 2003 Abstract #302010
Activity Number: 96
Type: Contributed
Date/Time: Monday, August 4, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #302010
Title: Analyzing Neurodegenerative Diseases with Multistate Models
Author(s): Kati Illouz*+ and Sudeshna Adak and Samit Paul and William Gorman and Jeffrey Kaye, M.D.
Companies: GE Research Centre and GE Global Research and GE John F Welch and GE Global Research and Oregon Health and Science University
Address: 1 Research Cir., Applied Statistics Lab, Niskayuna, NY, 12309-1027,
Keywords: neurodegenerative ; multistate models ; Alzheimer's ; adaptable ; imaging ; longitudinal
Abstract:

Multistate models (MSMs) have been proven to be successful in various areas of medical and epidemiological applications; however, their use in the field of neurology has been minimal to date. Longitudinal data from neurodegenerative diseases (ND) such as Alzheimer's and Huntington diseases have been typically analyzed using mixed effect models. Although these models are well-suited for the analysis of longitudinal data arising from studies on ND, they lack the flexibility in addressing some key data issues and complexities pertaining to these staged-bound diseases. Inference of the true disease stage based on surrogate measures, presence of missing data, interval censored observations, and the varying effects of covariates on alternating disease stage transitions are the driving issues towards more adaptable models. We present an application of MSMs as the alternative for analyzing longitudinal ND data, but more precisely for predicting at the patient level the course of the disease and the associated stage duration and transition times based on imaging, clinical and genetic covariates as well as patient characteristics.

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