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      A risk-based measure of time-varying prognostic discrimination for survival models

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          Summary

          Prognostic survival models are commonly evaluated in terms of both their calibration and their discrimination. Comparing observed and predicted survival curves can assess calibration, while discrimination is typically summarized through comparison of the properties of cases or subjects who experience an event, and the properties of controls represented by event-free individuals. For binary data, discrimination is characterized either by using the relative ranks of cases and controls and a receiver operating characteristic (ROC) curve, or by summarizing the magnitude of risk placed on cases and controls through calculation of the discrimination slope (DS). In this article we propose a risk-based measure of time-varying discrimination that generalizes the discrimination slope to allow use with incident events and hazard models. We refer to the new measure as the hazard discrimination summary (HDS) since it compares the relative risk among incident cases to their associated dynamic risk set controls. We introduce both a model-based estimation procedure that adopts the Cox model, and an alternative approach that locally relaxes the proportional hazards assumption. We illustrate the proposed methods using both a benchmark survival data set, and an oncology study where primary interest is in the time-varying performance of candidate biomarkers.

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          Author and article information

          Journal
          0370625
          1170
          Biometrics
          Biometrics
          Biometrics
          0006-341X
          1541-0420
          29 December 2016
          28 November 2016
          September 2017
          30 September 2017
          : 73
          : 3
          : 725-734
          Affiliations
          Department of Biostatistics, University of Washington, F-600 Health Sciences Building, Campus Mail Stop 357232, Seattle, Washington 98195-7232, U.S.A
          Author notes
          Article
          PMC5466878 PMC5466878 5466878 nihpa839309
          10.1111/biom.12628
          5466878
          27893925
          51e1e1cb-caba-407c-809a-57336a7644d7
          History
          Categories
          Article

          Predictive accuracy,Biomarkers,Cox regression,Discrimination,Survival,Longitudinal analysis

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