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Assessing predictive discrimination performance of biomarkers in the presence of treatment‐induced dependent censoring

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  • Cuihong Zhang
  • Jing Ning
  • Steven H. Belle
  • Robert H. Squires
  • Jianwen Cai
  • Ruosha Li

Abstract

In medical studies, some therapeutic decisions could lead to dependent censoring for the survival outcome of interest. This is exemplified by a study of paediatric acute liver failure, where death was subject to dependent censoring due to liver transplantation. Existing methods for assessing the predictive performance of biomarkers often pose the independent censoring assumption and are thus not applicable. In this work, we propose to tackle the dependence between the failure event and dependent censoring event using auxiliary information in multiple longitudinal risk factors. We propose estimators of sensitivity, specificity and area under curve, to discern the predictive power of biomarkers for the failure event by removing the disturbance of dependent censoring. Point estimation and inferential procedures were developed by adopting the joint modelling framework. The proposed methods performed satisfactorily in extensive simulation studies. We applied them to examine the predictive value of various biomarkers and risk scores for mortality in the motivating example.

Suggested Citation

  • Cuihong Zhang & Jing Ning & Steven H. Belle & Robert H. Squires & Jianwen Cai & Ruosha Li, 2022. "Assessing predictive discrimination performance of biomarkers in the presence of treatment‐induced dependent censoring," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1137-1157, November.
    • Handle: RePEc:bla:jorssc:v:71:y:2022:i:5:p:1137-1157
    DOI: 10.1111/rssc.12571
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    References listed on IDEAS

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    1. Dimitris Rizopoulos, 2011. "Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 67(3), pages 819-829, September.
    2. Yi‐Hau Chen, 2010. "Semiparametric marginal regression analysis for dependent competing risks under an assumed copula," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(2), pages 235-251, March.
    3. Lu Tian & Tianxi Cai & Els Goetghebeur & L. J. Wei, 2007. "Model evaluation based on the sampling distribution of estimated absolute prediction error," Biometrika, Biometrika Trust, vol. 94(2), pages 297-311.
    4. Yingye Zheng & Tianxi Cai & Yuying Jin & Ziding Feng, 2012. "Evaluating Prognostic Accuracy of Biomarkers under Competing Risk," Biometrics, The International Biometric Society, vol. 68(2), pages 388-396, June.
    5. James M. Robins & Dianne M. Finkelstein, 2000. "Correcting for Noncompliance and Dependent Censoring in an AIDS Clinical Trial with Inverse Probability of Censoring Weighted (IPCW) Log-Rank Tests," Biometrics, The International Biometric Society, vol. 56(3), pages 779-788, September.
    6. Ruosha Li & Limin Peng, 2017. "Assessing quantile prediction with censored quantile regression models," Biometrics, The International Biometric Society, vol. 73(2), pages 517-528, June.
    7. Xuelin Huang & Nan Zhang, 2008. "Regression Survival Analysis with an Assumed Copula for Dependent Censoring: A Sensitivity Analysis Approach," Biometrics, The International Biometric Society, vol. 64(4), pages 1090-1099, December.
    8. Rizopoulos, Dimitris, 2010. "JM: An R Package for the Joint Modelling of Longitudinal and Time-to-Event Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i09).
    9. Patrick J. Heagerty & Thomas Lumley & Margaret S. Pepe, 2000. "Time-Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker," Biometrics, The International Biometric Society, vol. 56(2), pages 337-344, June.
    10. Ming Wang & Qi Long, 2016. "Addressing issues associated with evaluating prediction models for survival endpoints based on the concordance statistic," Biometrics, The International Biometric Society, vol. 72(3), pages 897-906, September.
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