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Performance Comparison of Cumulative Incidence Estimators in the Presence of Competing Risks

경쟁위험 하에서의 누적발생함수 추정량 성능 비교

  • Kim, Dong-Uk (Department of Statistics, Sungkyunkwan University) ;
  • Ahn, Chi-Kyung (Department of Statistics, Sungkyunkwan University)
  • Published : 2007.07.31

Abstract

For the time-to-failure data with competing risks, cumulative incidence functions (CIFs) are commonly estimated using nonparametric methods. If the cases of events due to the cause of primary interest are infrequent relative to other cause of failure, nonparametric methods may result in rather imprecise estimates for CIF. In such cases, Bryant et al. (2004) suggested to model the cause-specific hazard of primary interest parametrically, while accounting for the other modes of failure using nonparametric estimator. We represented the semiparametric cumulative incidence estimator and extended to the model of Weibull and log-normal distribution. We also conducted simulations to access the performance of the semiparametric cumulative incidence estimators and to investigate the impact of model misspecification in log-normal cause-specific hazard model.

경쟁위험(competing risk) 하에서의 누적 발생함수(cumulative incidence function)는 일반적으로 비모수적 방법으로 추정된다. 그러나 관심 있는 원인에 의한 사건이 다른 원인에 의한 사건보다 상대적으로 적게 발생하는 경우에 비모수적 방법으로 추정된 누적발생함수는 이산성으로 인해 다소 정확하지 않게 된다. 이와 같은 경우에 Bryant와 Diagnam(2004)는 관심 있는 원인에 대한 원인특정적 위험함수(cause-specific hazard function)를 모수적으로 모형화하고 다른 원인에 의한 사건은 비모수적으로 추정하는 준모수적 방법을 제안했다. 본 연구에서는 준모수적 누적발생함수 추정량을 재표현하고 와이블분포모형과 대수 정규분포모형으로 확장하였다. 또한 대수 정규분포 원인특정적 위험모형일 경우 누적 발생함수에 대한 비모수적 추정량, 와이블분포 준모수적 추정량과 대수 정규분포 준모수적 추정량의 효율성을 비교하며 준모수적 추정량의 성능과 모형 오설정이 미치는 영향을 살펴보았다.

Keywords

References

  1. Bryant, J. and Diagnam, J. J. (2004). Semiparametric models for cumulative incidence functions, Biometrics, 60, 182-190 https://doi.org/10.1111/j.0006-341X.2004.00149.x
  2. Gaynor, J. J., Feuer, E. J., Tan, C. C., Wu, D. H., Little, C. R., Straus, D. J., Clarkson, B. D. and Brennan, M. F. (1993). On the use of cause-specific failure and conditional failure probabilities: examples from clinical oncology data, Journal of the American Statistical Association, 88, 400-409 https://doi.org/10.2307/2290318
  3. Gooley, T. A., Leisenring, W., Crowley, J. and Storer B. E. (1999). Estimation of failure probabilities in the presence of competing risks; new representations of old estimators, Statistics in Medicine, 18, 695-706 https://doi.org/10.1002/(SICI)1097-0258(19990330)18:6<695::AID-SIM60>3.0.CO;2-O
  4. Gray, R. J. (1988). A class of K-sample tests for comparing the cumulative incidence of a competing risk, The Annals of Statistics, 16, 1141-1154 https://doi.org/10.1214/aos/1176350951
  5. Kalbfleisch, J. D. and Prentice, R. L. (1980). The Statistical Analysis of Failure Time Data, John Wiley & Sons, New York
  6. Korn, E. L. and Dorey, F. J. (1992). Applications of crude incidence curves, Statistics in Medicine, 11, 813-829 https://doi.org/10.1002/sim.4780110611
  7. Marubini, E. and Valsecchi, M. G. (1995). Analysing Survival Data from Clinical Trials and Observational Studies, John Wiley & Sons, England
  8. Pepe, M. S. and Mori, M. (1993). Kaplan-Meier, marginal, or conditional probability curves in summarizing competing risks failure time data, Statistics in Medicine, 12, 737-751 https://doi.org/10.1002/sim.4780120803
  9. Prentice, R. L., Kalbfleisch, J. D., Peterson, A. V., Jr, Flournoy, N., Farewell, V. T. and Breslow, N. E. (1978). The analysis of failure times in the presence of competing risks, Biometrics, 34, 541-554 https://doi.org/10.2307/2530374
  10. Tsiatis, A. (1975). A nonidentifiability aspect of the problem of competing risks, Proceedings of the National Academy of Science, 72, 20-22