ArticleBiostatistics & epidemiology2017
Application of Concordance Probability Estimate to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease.
Article in Biostatistics & epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction Models for Conversion From Mild Cognitive Impairment to Alzheimer's Disease: A Systematic Review and Meta-Analysis.Frontiers in aging neuroscience · 2022Pooled it
- Joint ensemble learning-based risk prediction of Alzheimer's disease among mild cognitive impairment patients.The journal of prevention of Alzheimer's disease · 2025Article
- Variable selection for high-dimensional partly linear additive Cox model with application to Alzheimer's disease.Statistics in medicine · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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 & 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) dataset, we built a Cox PH model to predict the conversion from MCI to AD where the prognostic accuracy was evaluated using K-index.
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Registered trials
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