Inference for reclassification statistics under nested and non-nested models for biomarker evaluation.

Published

Journal Article

The Net Reclassification Improvement (NRI) and the Integrated Discrimination Improvement (IDI) are used to evaluate the diagnostic accuracy improvement for biomarkers in a wide range of applications. Most applications for these reclassification metrics are confined to nested model comparison. We emphasize the important extensions of these metrics to the non-nested comparison. Non-nested models are important in practice, in particular, in high-dimensional data analysis and in sophisticated semiparametric modeling. We demonstrate that the assessment of accuracy improvement may follow the familiar NRI and IDI evaluation. While the statistical properties of the estimators for NRI and IDI have been well studied in the nested setting, one cannot always rely on these asymptotic results to implement the inference procedure for practical data, especially for testing the null hypothesis of no improvement, and these properties have not been established for the non-nested setting. We propose a generic bootstrap re-sampling procedure for the construction of confidence intervals and hypothesis tests. Extensive simulations and real biomedical data examples illustrate the applicability of the proposed inference methods for both nested and non-nested models.

Full Text

Duke Authors

Cited Authors

  • Shao, F; Li, J; Fine, J; Wong, WK; Pencina, M

Published Date

  • 2015

Published In

Volume / Issue

  • 20 / 4

Start / End Page

  • 240 - 252

PubMed ID

  • 26301882

Pubmed Central ID

  • 26301882

Electronic International Standard Serial Number (EISSN)

  • 1366-5804

Digital Object Identifier (DOI)

  • 10.3109/1354750X.2015.1068854

Language

  • eng

Conference Location

  • England