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Estimation of the time-dependent AUC for cure rate model with covariate dependent censoring

  • Yang-Jin Kim (Department of Statistics, Sookmyung Women's University)
  • Received : 2023.10.23
  • Accepted : 2024.02.20
  • Published : 2024.07.31

Abstract

Diverse methods to evaluate the prediction model of a time to event have been proposed in the context of right censored data where all subjects are subject to be susceptible. A time-dependent AUC (area under curve) measures the predictive ability of a marker based on case group and control one which are varying over time. When a substantial portion of subjects are event-free, a population consists of a susceptible group and a cured one. An uncertain curability of censored subjects makes it difficult to define both case group and control one. In this paper, our goal is to propose a time-dependent AUC for a cure rate model when a censoring distribution is related with covariates. A class of inverse probability of censoring weighted (IPCW) AUC estimators is proposed to adjust the possible sampling bias. We evaluate the finite sample performance of the suggested methods with diverse simulation schemes and the application to the melanoma dataset is presented to compare with other methods.

Keywords

Acknowledgement

This research is supported by Korean research foundation (NRF-2020R1A2C1A01100755).

References

  1. Alonzo TA and Pepe MS (2005). Assessing accuracy of a continuous screening test in the presence of verification bias, Journal of the Royal Statistical Society Series C: Applied Statistics, 54, 173-190. https://doi.org/10.1111/j.1467-9876.2005.00477.x
  2. Asano J, Hirakawa A, and Hamada C (2014). Assessing the prediction accuracy of cure in the Cox proportional hazards cure model: An application to breast cancer data, Pharmaceutical Statistics, 13, 357-363. https://doi.org/10.1002/pst.1630
  3. Asano J and Hirakawa A (2017). Assessing the prediction accuracy of a cure model for censored survival data with long-term survivors: Application to breast cancer data, Journal of Biopharmaceutical Statistics, 27, 918-932. https://doi.org/10.1080/10543406.2017.1293082
  4. Beyene KM, Ghouch AE, and Oulhaj A (2019). On the validity of time-dependent AUC estimation in the presence of cure fraction, Biometrical Journal, 61, 1430-1447. https://doi.org/10.1002/bimj.201800376
  5. Blache P, Dartigues J, and Jacqmin-Gadda H (2013). Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks, Statistics in Medicine, 32, 5381-5397. https://doi.org/10.1002/sim.5958
  6. Fine JP and Gray RJ (1999). A proportional hazards model for the subdistribution of a competing risk, Journal of the American Statistical Association, 446, 496-509. https://doi.org/10.1080/01621459.1999.10474144
  7. Gerds TA, Kattan MW, Schumacher M, and Yu C (2013). Estimating a time-dependent concordance index for survival prediction models with covariate dependent censoring, Statistics in Medicine, 32, 2173-2184. https://doi.org/10.1002/sim.5681
  8. Heagerty PJ and Zhang Y (2005). Survival model predictive accuracy and ROC curves, Biometrics, 61, 92-105. https://doi.org/10.1111/j.0006-341X.2005.030814.x
  9. Kamarudin AN, Cox T, and Kolamunnage-Dona R (2017). Time-dependent ROC curve analysis in medical research: Current methods and applications, BMC Medical Research Methodology, 17, 53.
  10. Kim YJ and Jhun M (2008). Cure rate model for interval censored data, Statistics in Medicine, 27, 3-14. https://doi.org/10.1002/sim.2918
  11. Kuk AYC and Chen C (1992). A mixture model combining logistic regression with proportional hazards regression, Biometrika, 79, 531-541. https://doi.org/10.1093/biomet/79.3.531
  12. Li L, Greene T, and Hu B (2018). A simple method to estimate the time-dependent receiver operating characteristic curve and the area under the curve with right censored data, Statistical Methods in Medical Research, 27, 2264-2278. https://doi.org/10.1177/0962280216680239
  13. Maller RA and Zhou X (1996). Survival Analysis with Long-term Survivors, Wiley, New York.
  14. Pepe MS (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction, Oxford University Press, USA.
  15. Rizopoulos D, Molenberghs G, Emmanuel MEH, and Lesaffre E (2017). Dynamic predictions with time-dependent covariates in survival analysis using joint modeling and landmarking, Biometrical Journal, 59, 1261-1276. https://doi.org/10.1002/bimj.201600238
  16. Robins JM (1993). Information recovery and bias adjustment in proportional hazards regression analysis of randomized trials using surrogate markers. In Proceedings of the Biopharmaceutical Section, American Statistical Association, Alexandria, Virginia, 24-33.
  17. Robins JM and Finkelstein DM (2000). Correcting for noncompliance and dependent censoring in an AIDS clinical trial with inverse probability of censoring weighted (IPCW) log-rank tests, Biometrics, 56, 779-788. https://doi.org/10.1111/j.0006-341X.2000.00779.x
  18. Sy JP and Taylor JM G (2000). Estimation in a Cox proportional hazards cure model, Biometrics, 56, 227-236. https://doi.org/10.1111/j.0006-341X.2000.00227.x
  19. Uno H, Cai T, Pencina MJ, D'Agostino RB, and Wei LJ (2011). On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data, Statistics in Medicine, 30, 1105-1117. https://doi.org/10.1002/sim.4154
  20. Wang Z and Wang X (2020). Evaluating the time-dependent predictive accuracy for event-to-time outcome with a cure fraction, Pharmaceutical Statistics, 19, 955-974. https://doi.org/10.1002/pst.2048
  21. Yakovlev AY, Asselain B, Bardou VJ, Fourquet A, Hoang T, Rochefediere A, and Tsodikov AD (1993). A simple stochastic model of tumor recurrence and its application to data on premenopausal breast cancer, Biometrie et Analyse de Donnees Spatio-temporelles, 12, 66-82.