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Validation of Clinical Classification Schemes for Predicting Stroke: Results From the National Registry of Atrial Fibrillation

TLDR
The 2 existing classification schemes and especially a new stroke risk index, CHADS, can quantify risk of stroke for patients who have AF and may aid in selection of antithrombotic therapy.
Abstract
a c statistic of 0.82 (95% CI, 0.80-0.84), the CHADS2 index was the most accurate predictor of stroke. The stroke rate per 100 patient-years without antithrombotic therapy increased by a factor of 1.5 (95% CI, 1.3-1.7) for each 1-point increase in the CHADS2 score: 1.9 (95% CI, 1.2-3.0) for a score of 0; 2.8 (95% CI, 2.0-3.8) for 1; 4.0 (95% CI, 3.1-5.1) for 2; 5.9 (95% CI, 4.6-7.3) for 3; 8.5 (95% CI, 6.3-11.1) for 4; 12.5 (95% CI, 8.2-17.5) for 5; and 18.2 (95% CI, 10.5-27.4) for 6. Conclusion The 2 existing classification schemes and especially a new stroke risk index, CHADS2, can quantify risk of stroke for patients who have AF and may aid in selection of antithrombotic therapy.

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Journal ArticleDOI

Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors

TL;DR: In this article, an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities are discussed, which are particularly needed for binary, ordinal, and time-to-event outcomes.
Book

Statistical Methods for Survival Data Analysis

Elisa T. Lee
TL;DR: The Fourth Edition of Statistical Methods for Survival Data Analysis is an ideal text for upper-undergraduate and graduate-level courses on survival data analysis and is an excellent resource for biomedical investigators, statisticians, and epidemiologists, as well as researchers in every field in which the analysis of survival data plays a role.
Book

Bootstrapping: A Nonparametric Approach to Statistical Inference

TL;DR: In this paper, the authors present a formal justification for the use of the Bootstrap in statistical inference. But they do not discuss future limitations of the bootstrap and their application in the statistical verification of confidence intervals.
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