Analysis of conversion of Alzheimer's disease using a multi-state Markov model. (September 2019)

• Record Type: Journal Article
• Authors: Zhang, Liangliang; Lim, Chae Young; Maiti, Tapabrata; Li, Yingjie; Choi, Jongeun; Bozoki, Andrea; Zhu, David C.
• Is Part Of: Statistical methods in medical research. Volume 28:Number 9(2019)
• Page Start: 2801
• Page End: 2819
• Publication Date: 2019-09
• Subjects: Alzheimer's disease -- brain structural volumetric variables -- left truncation -- Markov model -- magnetic resonance imaging scan -- transition probability -- survival probability -- prediction; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280218786525
• Languages: English
• ISSNs: 0962-2802

Abstract

With rapid aging of world population, Alzheimer's disease is becoming a leading cause of death after cardiovascular disease and cancer. Nearly 10% of people who are over 65 years old are affected by Alzheimer's disease. The causes have been studied intensively, but no definitive answer has been found. Genetic predisposition, abnormal protein deposits in brain, and environmental factors are suspected to play a role in the development of this disease. In this paper, we model progression of Alzheimer's disease using a multi-state Markov model to investigate the significance of known risk factors such as age, apolipoprotein E4, and some brain structural volumetric variables from magnetic resonance imaging scans (e.g., hippocampus, etc.) while predicting transitions between different clinical diagnosis states. With the Alzheimer's Disease Neuroimaging Initiative data, we found that the model with age is not significant (p = 0.1733) according to the likelihood ratio test, but the apolipoprotein E4 is a significant risk factor, and the examination of apolipoprotein E4-by-sex interaction suggests that the apolipoprotein E4 link to Alzheimer's disease is stronger in women. Given the estimated transition probabilities, the prediction accuracy is as high as 0.7849.