Analysis of conversion of Alzheimer’s disease using a multi-state Markov model

for the Key Laboratory of Applied Statistics of the Ministry of Education (KLAS), and for the Alzheimer’s Disease Neuroimaging Initiative

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2801-2819
Number of pages19
JournalStatistical Methods in Medical Research
Volume28
Issue number9
DOIs
Publication statusPublished - 2019 Sep 1

Fingerprint

Multi-state Model
Alzheimer's Disease
Apolipoprotein E4
Markov Model
Alzheimer Disease
Risk Factors
Neuroimaging
Hippocampus
Environmental Factors
Magnetic Resonance Imaging
Brain
Likelihood Ratio Test
Genetic Predisposition to Disease
Transition Probability
Progression
Cause of Death
Cancer
Cardiovascular Diseases
Protein
Prediction

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

Cite this

for the Key Laboratory of Applied Statistics of the Ministry of Education (KLAS), & and for the Alzheimer’s Disease Neuroimaging Initiative (2019). Analysis of conversion of Alzheimer’s disease using a multi-state Markov model. Statistical Methods in Medical Research, 28(9), 2801-2819. https://doi.org/10.1177/0962280218786525
for the Key Laboratory of Applied Statistics of the Ministry of Education (KLAS) ; and for the Alzheimer’s Disease Neuroimaging Initiative. / Analysis of conversion of Alzheimer’s disease using a multi-state Markov model. In: Statistical Methods in Medical Research. 2019 ; Vol. 28, No. 9. pp. 2801-2819.
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for the Key Laboratory of Applied Statistics of the Ministry of Education (KLAS) & and for the Alzheimer’s Disease Neuroimaging Initiative 2019, 'Analysis of conversion of Alzheimer’s disease using a multi-state Markov model', Statistical Methods in Medical Research, vol. 28, no. 9, pp. 2801-2819. https://doi.org/10.1177/0962280218786525

Analysis of conversion of Alzheimer’s disease using a multi-state Markov model. / for the Key Laboratory of Applied Statistics of the Ministry of Education (KLAS); and for the Alzheimer’s Disease Neuroimaging Initiative.

In: Statistical Methods in Medical Research, Vol. 28, No. 9, 01.09.2019, p. 2801-2819.

Research output: Contribution to journalArticle

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for the Key Laboratory of Applied Statistics of the Ministry of Education (KLAS), and for the Alzheimer’s Disease Neuroimaging Initiative. Analysis of conversion of Alzheimer’s disease using a multi-state Markov model. Statistical Methods in Medical Research. 2019 Sep 1;28(9):2801-2819. https://doi.org/10.1177/0962280218786525