NDLI: Evaluating incremental values from new predictors with net reclassification improvement in survival analysis

Content Provider	Springer Nature Link
Author	Zheng, Yingye Parast, Layla Cai, Tianxi Brown, Marshall
Copyright Year	2012
Abstract	Developing individualized prediction rules for disease risk and prognosis has played a key role in modern medicine. When new genomic or biological markers become available to assist in risk prediction, it is essential to assess the improvement in clinical usefulness of the new markers over existing routine variables. Net reclassification improvement (NRI) has been proposed to assess improvement in risk reclassification in the context of comparing two risk models and the concept has been quickly adopted in medical journals (Pencina et al., Stat Med 27:157–172, 2008). We propose both nonparametric and semiparametric procedures for calculating NRI as a function of a future prediction time $$t$$ with a censored failure time outcome. The proposed methods accommodate covariate-dependent censoring, therefore providing more robust and sometimes more efficient procedures compared with the existing nonparametric-based estimators (Pencina et al., Stat Med 30:11–21, 2011; Uno et al., Comparing risk scoring systems beyond the roc paradigm in survival analysis, 2009). Simulation results indicate that the proposed procedures perform well in finite samples. We illustrate these procedures by evaluating a new risk model for predicting the onset of cardiovascular disease.
Starting Page	350
Ending Page	370
Page Count	21
File Format	PDF
ISSN	13807870
Journal	Lifetime Data Analysis
Volume Number	19
Issue Number	3
e-ISSN	15729249
Language	English
Publisher	Springer US
Publisher Date	2012-12-20
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Inverse probability weighted (IPW) estimator Net reclassification improvement (NRI) Risk prediction Survival analysis Statistics Statistics for Life Sciences, Medicine, Health Sciences Quality Control, Reliability, Safety and Risk Statistics for Business/Economics/Mathematical Finance/Insurance Operations Research/Decision Theory
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Medicine

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