Application of concordance probability estimate to predict conversion from mild cognitive impairment to Alzheimer's disease. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Han, Xiaoxia; Zhang, Yilong; Shao, Yongzhao
• Is Part Of: Biostatistics & epidemiology. Volume 1:Issue 1(2017)
• Page Start: 105
• Page End: 118
• Publication Date: 2017-01-01
• Subjects: Alzheimer's disease -- mild cognitive impairment -- risk prediction model -- prognostic accuracy -- concordance probability; Epidemiology -- Periodicals; Biometry -- Periodicals; 614.42
• DOI: 10.1080/24709360.2017.1342187
• Languages: English
• ISSNs: 2470-9360

Abstract

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.