Lifetime Data Analysis

, Volume 19, Issue 2, pp 142–169

Subgroup specific incremental value of new markers for risk prediction


DOI: 10.1007/s10985-012-9235-3

Cite this article as:
Zhou, Q.M., Zheng, Y. & Cai, T. Lifetime Data Anal (2013) 19: 142. doi:10.1007/s10985-012-9235-3


In many clinical applications, understanding when measurement of new markers is necessary to provide added accuracy to existing prediction tools could lead to more cost effective disease management. Many statistical tools for evaluating the incremental value (IncV) of the novel markers over the routine clinical risk factors have been developed in recent years. However, most existing literature focuses primarily on global assessment. Since the IncVs of new markers often vary across subgroups, it would be of great interest to identify subgroups for which the new markers are most/least useful in improving risk prediction. In this paper we provide novel statistical procedures for systematically identifying potential traditional-marker based subgroups in whom it might be beneficial to apply a new model with measurements of both the novel and traditional markers. We consider various conditional time-dependent accuracy parameters for censored failure time outcome to assess the subgroup-specific IncVs. We provide non-parametric kernel-based estimation procedures to calculate the proposed parameters. Simultaneous interval estimation procedures are provided to account for sampling variation and adjust for multiple testing. Simulation studies suggest that our proposed procedures work well in finite samples. The proposed procedures are applied to the Framingham Offspring Study to examine the added value of an inflammation marker, C-reactive protein, on top of the traditional Framingham risk score for predicting 10-year risk of cardiovascular disease.


Incremental value Partial area under the ROC curve   Prognostic accuracy Risk prediction Subgroup analysis   Time dependent ROC analysis 

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Department of Statistics and Actuarial ScienceSimon Fraser UniversityBurnabyCanada
  2. 2.Public Health Sciences DivisionFred Hutchinson Cancer Research CenterSeattleUSA
  3. 3.Department of BiostatisticsHarvard UniversityBostonUSA

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