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A unified Bayesian semiparametric approach to assess discrimination ability in survival analysis
Author(s) -
Zhao Lili,
Feng Dai,
Chen Guoan,
Taylor Jeremy M. G.
Publication year - 2016
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12453
Subject(s) - bayesian probability , econometrics , survival analysis , computer science , semiparametric model , semiparametric regression , statistics , mathematics , nonparametric statistics
Summary The discriminatory ability of a marker for censored survival data is routinely assessed by the time‐dependent ROC curve and the c ‐index. The time‐dependent ROC curve evaluates the ability of a biomarker to predict whether a patient lives past a particular time t . The c ‐index measures the global concordance of the marker and the survival time regardless of the time point. We propose a Bayesian semiparametric approach to estimate these two measures. The proposed estimators are based on the conditional distribution of the survival time given the biomarker and the empirical biomarker distribution. The conditional distribution is estimated by a linear‐dependent Dirichlet process mixture model. The resulting ROC curve is smooth as it is estimated by a mixture of parametric functions. The proposed c ‐index estimator is shown to be more efficient than the commonly used Harrell's c ‐index since it uses all pairs of data rather than only informative pairs. The proposed estimators are evaluated through simulations and illustrated using a lung cancer dataset.