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

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 [Formula: see text] 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.

SUBMITTER: Zheng Y

PROVIDER: S-EPMC3686882 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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

Zheng Yingye Y Parast Layla L Cai Tianxi T Brown Marshall M

Lifetime data analysis 20121220 3

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 ...[more]

PMID: 23254468

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Project description:Net reclassification indices have recently become popular statistics for measuring the prediction increment of new biomarkers. We review the various types of net reclassification indices and their correct interpretations. We evaluate the advantages and disadvantages of quantifying the prediction increment with these indices. For predefined risk categories, we relate net reclassification indices to existing measures of the prediction increment. We also consider statistical methodology for constructing confidence intervals for net reclassification indices and evaluate the merits of hypothesis testing based on such indices. We recommend that investigators using net reclassification indices should report them separately for events (cases) and nonevents (controls). When there are two risk categories, the components of net reclassification indices are the same as the changes in the true- and false-positive rates. We advocate the use of true- and false-positive rates and suggest it is more useful for investigators to retain the existing, descriptive terms. When there are three or more risk categories, we recommend against net reclassification indices because they do not adequately account for clinically important differences in shifts among risk categories. The category-free net reclassification index is a new descriptive device designed to avoid predefined risk categories. However, it experiences many of the same problems as other measures such as the area under the receiver operating characteristic curve. In addition, the category-free index can mislead investigators by overstating the incremental value of a biomarker, even in independent validation data. When investigators want to test a null hypothesis of no prediction increment, the well-established tests for coefficients in the regression model are superior to the net reclassification index. If investigators want to use net reclassification indices, confidence intervals should be calculated using bootstrap methods rather than published variance formulas. The preferred single-number summary of the prediction increment is the improvement in net benefit.

Dataset Information

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

Publications

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

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