응용통계연구 (The Korean Journal of Applied Statistics)

  • 제31권6호
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  • Pages.761-769
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  • 2018
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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Cox 비례위험모형을 따르는 중도절단자료 생성

Generating censored data from Cox proportional hazards models

  • 김지현 (숭실대학교 정보통계보험수리학과) ;
  • 김봉성 (숭실대학교 정보통계보험수리학과)
  • Kim, Ji-Hyun (Department of Statistics and Actuarial Science, Soongsil University) ;
  • Kim, Bongseong (Department of Statistics and Actuarial Science, Soongsil University)
  • 투고 : 2018.09.14
  • 심사 : 2018.11.02
  • 발행 : 2018.12.31
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초록

통계학 연구에 모의실험이 중요하게 쓰이며 중도절단자료를 다루는 생존분석에서도 마찬가지다. 생존분석에서 Cox 모형이 널리 쓰이는데, Cox 모형을 따르는 중도절단자료를 생성하는 방법에 대해 살펴보았다. Bender 등 (Statistics in Medicine, 24, 1713-1723, 2005)은 생존시간을 생성하는 모수적 방법을 제시하였으나 생존시간뿐만 아니라 중도절단시간도 생성해야 중도절단자료를 얻게 된다. 중도절단자료를 생성하기 위한 모수적 방법과 함께 비모수적 방법도 제시하였으며 실제 자료에도 적용해 보았다.

Simulations are important for survival analyses that deal with censored data. Cox models are widely used in survival analyses, therefore, we investigate how to generate censored data that can simulate the Cox model. Bender et al. (Statistics in Medicine, 24, 1713-1723, 2005) provided a parametric method for generating survival times, but we need to generate censoring times as well as survival times to simulate the censored data. In addition to the parametric method for generating censored data, a nonparametric method is also proposed and applied to a real data set.

키워드

Table 2.1. Execution time taken to generate one survival time (in microseconds)

GCGHDE_2018_v31n6_761_t0001.png 이미지

Table 3.1. Cox model for the observed data

GCGHDE_2018_v31n6_761_t0002.png 이미지

Table 3.2. The mean and 95% confidence interval of the regression coefficient for CLINIC2, obtained from 400 iterations of simulation

GCGHDE_2018_v31n6_761_t0003.png 이미지

참고문헌

  1. Bender, R., Augustin, T., and Blettner, M. (2005). Generating survival times to simulate Cox proportional hazards models, Statistics in Medicine, 24, 1713-1723. https://doi.org/10.1002/sim.2059
  2. Breslow, N. (1974). Covariance analysis of censored survival data, Biometrics, 30, 89-99. https://doi.org/10.2307/2529620
  3. Caplehorn, J. R. and Bell, J. (1991). Methadone dosage and retention of patients in maintenance treatment, The Medical Journal of Australia, 154, 195-199.
  4. Cox, D. R. (1972). Regression models and life tables (with discussion), Journal of the Royal Statistical Society, Series B, 34, 187-220.
  5. Harrell Jr, F. with contributions from Charles Dupont and many others (2017). Hmisc: Harrell Miscellaneous. R package version 4.0-3. https://CRAN.R-project.org/package=Hmisc
  6. Kropko, J. and Harden, J. (2018). coxed: Duration-Based Quantities of Interest for the Cox Proportional Hazards Model. R package version 0.2.0. https://CRAN.R-project.org/package=coxed
  7. Mersmann, O. (2018). microbenchmark: Accurate Timing Functions. R package version 1.4-4. https://CRAN.R-project.org/package=microbenchmark
  8. Morina, D. and Navarro, A. (2014). The R package survsim for the simulation of simple and complex survival data, Journal of Statistical Software, 59, 1-20.