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On Ranking in Survival Analysis: Bounds on the Concordance Index
Vikas C Raykar · Harald Steck · Balaji R Krishnapuram · Cary Dehing-Oberije · Philippe Lambin

Mon Dec 03 10:30 AM -- 10:40 AM (PST) @

In this paper, we show that classical survival analysis involving censored data can naturally be cast as a ranking problem. The concordance index (CI), which quantifies the quality of rankings, is the standard performance measure for model \emph{assessment} in survival analysis. In contrast, the standard approach to \emph{learning} the popular proportional hazard (PH) model is based on Cox's partial likelihood. In this paper we devise two bounds on CI--one of which emerges directly from the properties of PH models--and optimize them \emph{directly}. Our experimental results suggest that both methods perform about equally well, with our new approach giving slightly better results than the Cox's method. We also explain why a method designed to maximize the Cox's partial likelihood also ends up (approximately) maximizing the CI.

Author Information

Vikas C Raykar (IBM Research)
Harald Steck (ETH Zurich)
Balaji R Krishnapuram (IBM)
Cary Dehing-Oberije
Philippe Lambin

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