The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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A two-sample test with interval censored competing risk data using multiple imputation

다중대체방법을 이용한 구간 중도 경쟁 위험 모형에서의 이표본 검정

  • Kim, Yuwon (Department of Statistics, Sookmyung Women's University) ;
  • Kim, Yang-Jin (Department of Statistics, Sookmyung Women's University)
  • 김유원 (숙명여자대학교 통계학과) ;
  • 김양진 (숙명여자대학교 통계학과)
  • Received : 2017.01.10
  • Accepted : 2017.03.21
  • Published : 2017.04.30
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Abstract

Interval censored data frequently occur in observation studies where the subject is followed periodically. In this paper, our interest is to suggest a test statistic to compare the CIF of two groups with interval censored failure time data in the presence of competing risks. Gray (1988) suggested a test statistic for right censored data that motivated a well-known Fine and Gray's subdistribution hazard model. A multiple imputation technique is adopted to adopt Gray's test statistic to interval censored data. The powers and sizes of the suggested method are investigated through diverse simulation schemes. The main merit of the suggested method is its simplicity to implement with existing software for right censored data. The method is illustrated by analyzing Bangkok's HIV cohort dataset.

구간 중도 절단 자료는 관측 연구에서 종종 발생되는 생존 자료의 한 유형으로 관심 있는 사건 발생 시간을 정확하게 관측할 수 없는 대신에 이를 포함한 두 관측 시점으로 구성된다. 본 연구의 목적은 경쟁 위험이 구간 중도 절단 자료에서 발생될 경우, 두 그룹의 누적 발생 함수를 비교하기 위한 검정 통계량을 제시하는 것이다. 특히 본 연구에서는 다중 대체 방법을 통해 생성된 자료를 이용하여 검정력과 유의 수준을 구하고자 한다. 모의실험을 통해 제안한 방법이 다양한 경우에서 적절한 결과를 보이는지 검토하였으며 실제 자료 분석의 예로 남녀 그룹의 HIV 발생 함수의 차이를 비교하기 위해 제안한 방법을 적용하였다.

Keywords

References

  1. Bakoyannis, G., Siannis, F., and Touloumi, G. (2010). Modelling competing risks data with missing cause of failure, Statistics in Medicine, 29, 3172-3185. https://doi.org/10.1002/sim.4133
  2. Beyersmann, J., Latouche, A., Buchhols, A. and Schumacher, M. (2009). Simulating competing risk data in survival analysis, Statistics in Medicine, 28, 956-971. https://doi.org/10.1002/sim.3516
  3. Do, G. and Kim, Y.-J. (2017). Analysis of interval censored competing risk data with missing causes of failure using pseudo values approach, Journal of Statistical Computations and Simulations, In press
  4. Fine, J. P. and Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk, Journal of the American Statistical Association, 94, 496-509. https://doi.org/10.1080/01621459.1999.10474144
  5. Goetghebeur, E. and Ryan, L. (1995). Analysis of competing risks survival data when some failure types are missing, Biometrika, 82, 821-834. https://doi.org/10.1093/biomet/82.4.821
  6. Gray, R. J. (1988). A class of k-sample tests for comparing the cumulative incidence of a competing risk, Annals of Statistics, 16, 1141-1154. https://doi.org/10.1214/aos/1176350951
  7. Hudgens, M. G., Satten, G. A., and Longini, I. M. (2001). Nonparametric maximum likelihood estimation for competing risks survival data subject to interval censoring and truncation, Biometrics, 57, 74-80. https://doi.org/10.1111/j.0006-341X.2001.00074.x
  8. Jewell, N. P. and Kalb eisch, J. D. (2004). Maximum likelihood estimation of ordered multinomial parameters, Biostatistics, 5, 291-306. https://doi.org/10.1093/biostatistics/5.2.291
  9. Jewell, N. P., Van der Laan, M. J., and Henneman, T. (2003). Nonparametric estimation from current status data with competing risks, Biometrika, 90, 183-197. https://doi.org/10.1093/biomet/90.1.183
  10. Klein, J. P. and Andersen, P. K. (2005). Regression modeling of competing risks data based on pseudo values of the cumulative incidence function, Biometrics, 61, 223-229. https://doi.org/10.1111/j.0006-341X.2005.031209.x
  11. Lindsey, J. and Ryan, L. (1998). Methods for interval censored data, tutorial in biostatistics, Statistics in Medicine, 17, 219-138. https://doi.org/10.1002/(SICI)1097-0258(19980130)17:2<219::AID-SIM735>3.0.CO;2-O
  12. Lu, K. and Tsiatis, A. A. (2001). Multiple imputation methods for estimating regression coefficients in the competing risks model with missing cause of failure, Biometrics, 57, 1191-1197. https://doi.org/10.1111/j.0006-341X.2001.01191.x
  13. Pan, W. (2000a). A multiple imputation approach to cox regression with interval censored data, Biometrics, 56, 199-203. https://doi.org/10.1111/j.0006-341X.2000.00199.x
  14. Pan, W. (2000b). A two-sample test with interval censored data with multiple imputation, Statistics in Medicine, 19, 1-11. https://doi.org/10.1002/(SICI)1097-0258(20000115)19:1<1::AID-SIM296>3.0.CO;2-Q
  15. Ruan, P. K. and Gray, R. J.(2008). Analyses of cumulative incidence function via non-parametric multiple imputation, Statistics in Medicine, 27, 5709-5724. https://doi.org/10.1002/sim.3402
  16. Turnbull, B. W. (1976). The empirical distribution function with arbitrarily grouped censored and truncated data, Journal of the Royal Statistical Society Series B (Methodological), 38, 290-295.