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Optimal Composite Markers for Time‐Dependent Receiver Operating Characteristic Curves with Censored Survival Data

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  • HUNG HUNG
  • CHIN‐TSANG CHIANG

Abstract

. To increase the predictive abilities of several plasma biomarkers on the coronary artery disease (CAD)‐related vital statuses over time, our research interest mainly focuses on seeking combinations of these biomarkers with the highest time‐dependent receiver operating characteristic curves. An extended generalized linear model (EGLM) with time‐varying coefficients and an unknown bivariate link function is used to characterize the conditional distribution of time to CAD‐related death. Based on censored survival data, two non‐parametric procedures are proposed to estimate the optimal composite markers, linear predictors in the EGLM model. Estimation methods for the classification accuracies of the optimal composite markers are also proposed. In the article we establish theoretical results of the estimators and examine the corresponding finite‐sample properties through a series of simulations with different sample sizes, censoring rates and censoring mechanisms. Our optimization procedures and estimators are further shown to be useful through an application to a prospective cohort study of patients undergoing angiography.

Suggested Citation

  • Hung Hung & Chin‐Tsang Chiang, 2010. "Optimal Composite Markers for Time‐Dependent Receiver Operating Characteristic Curves with Censored Survival Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(4), pages 664-679, December.
    • Handle: RePEc:bla:scjsta:v:37:y:2010:i:4:p:664-679
    DOI: 10.1111/j.1467-9469.2009.00683.x
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    References listed on IDEAS

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    1. Susana Díaz-Coto & Pablo Martínez-Camblor & Sonia Pérez-Fernández, 2020. "smoothROCtime: an R package for time-dependent ROC curve estimation," Computational Statistics, Springer, vol. 35(3), pages 1231-1251, September.
    2. Wei Miao & Jad Beyhum & Jonas Striaukas & Ingrid Van Keilegom, 2025. "High-dimensional censored MIDAS logistic regression for corporate survival forecasting," Papers 2502.09740, arXiv.org, revised Feb 2026.

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