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Application of concordance probability estimate to predict conversion from mild cognitive impairment to Alzheimer's disease

Application of concordance probability estimate to predict conversion from mild cognitive... Subjects with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD). Identifying MCI subjects who have high progression risk to AD is important in clinical management. Existing risk prediction models of AD among MCI subjects generally use either the AUC or Harrell's C-statistic to evaluate predictive accuracy. AUC is aimed at binary outcome and Harrell's C-statistic depends on the unknown censoring distribution. Gönen and Heller's K-index, also known as concordance probability estimate (CPE), is another measure of overall predictive accuracy for Cox proportional hazards (PH) models, which does not depend on censoring distribution. As a comprehensive example, using Alzheimer's Disease Neuroimaging Initiative (ADNI) data-set, we built a Cox PH model to predict the conversion from MCI to AD where the prognostic accuracy was evaluated using K-index. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

Application of concordance probability estimate to predict conversion from mild cognitive impairment to Alzheimer's disease

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Application of concordance probability estimate to predict conversion from mild cognitive impairment to Alzheimer's disease

Abstract

Subjects with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD). Identifying MCI subjects who have high progression risk to AD is important in clinical management. Existing risk prediction models of AD among MCI subjects generally use either the AUC or Harrell's C-statistic to evaluate predictive accuracy. AUC is aimed at binary outcome and Harrell's C-statistic depends on the unknown censoring...
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Publisher
Taylor & Francis
Copyright
© 2017 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2017.1342187
Publisher site
See Article on Publisher Site

Abstract

Subjects with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD). Identifying MCI subjects who have high progression risk to AD is important in clinical management. Existing risk prediction models of AD among MCI subjects generally use either the AUC or Harrell's C-statistic to evaluate predictive accuracy. AUC is aimed at binary outcome and Harrell's C-statistic depends on the unknown censoring distribution. Gönen and Heller's K-index, also known as concordance probability estimate (CPE), is another measure of overall predictive accuracy for Cox proportional hazards (PH) models, which does not depend on censoring distribution. As a comprehensive example, using Alzheimer's Disease Neuroimaging Initiative (ADNI) data-set, we built a Cox PH model to predict the conversion from MCI to AD where the prognostic accuracy was evaluated using K-index.

Journal

Biostatistics & Epidemiology – Taylor & Francis

Published: Jan 1, 2017

Keywords: Alzheimer's disease; mild cognitive impairment; risk prediction model; prognostic accuracy; concordance probability

References

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