DOI QR코드

DOI QR Code

Statistical analysis of recurrent gap time events with incomplete observation gaps

불완전한 관측틈을 가진 재발 사건 소요시간에 대한 자료 분석

  • Shin, Seul Bi (Health Insurance Policy Research Institute, National Health Insurance Corporation) ;
  • Kim, Yang Jin (Department of Statistics, Sookmyung Women's University)
  • 신슬비 (국민건강보험공단 건강보험정책연구원) ;
  • 김양진 (숙명여자대학교 통계학과)
  • Received : 2013.12.30
  • Accepted : 2014.02.20
  • Published : 2014.03.31

Abstract

Recurrent event data occurs when a subject experiences same type of event repeatedly and is found in various areas such as the social sciences, Economics, medicine and public health. To analyze recurrent event data either a total time or a gap time is adopted according to research interest. In this paper, we analyze recurrent event data with incomplete observation gap using a gap time scale. That is, some subjects leave temporarily from a study and return after a while. But it is not available when the observation gaps terminate. We adopt an interval censoring mechanism for estimating the termination time. Furthermore, to model the association among gap times of a subject, a frailty effect is incorporated into a model. Programs included in Survival package of R program are implemented to estimate the covariate effect as well as the variance of frailty effect. YTOP (Young Traffic Offenders Program) data is analyzed with both proportional hazard model and a weibull regression model.

재발 사건 자료란 연구대상이 같은 종류의 사건을 반복적으로 경험할 때 발생하는 자료이다. 이러한 재발 사건은 사회과학, 자연과학, 공학, 의약학 등 다양한 분야에서 나타날 수 있다. 재발 사건자료를 분석할 때 연구자의 관심에 따라 사건 발생시간이나 사건 발생간의 소요시간을 이용하여 분석할 수 있다. 이 논문에서는 사건 발생시점간의 소요시간을 이용하여 불완전한 관측을 가진 재발 사건자료를 분석하고자 한다. 이 자료의 특징은 일부 관측대상들이 일정기간 동안 연구에서 제외되는 관측틈을 갖는다는 것이다. 이 때 관측틈은 불완전한 형태로 나타나게 되는데 그 이유는 관측틈의 시작시점은 알고 있지만 종료시점은 알 수 없기 때문이다. 이러한 미지의 종료시점을 추정하기 위해서 구간 중도 절단 방법이 적용된다. 따라서 종료시점이 추정된 후 프레일티를 포함한 회귀모형을 적용하여 공변량이 사건 재발에 미치는 영향을 알아볼 수 있다. 또한 제안한 방법을 실제자료에 적용하여 관측틈을 고려한 경우와 고려하지 않은 경우를 비교하고자 한다.

Keywords

References

  1. Cook, J. and Lawless, J. F. (2007). The statistical analysis of recurrent events, Springer, New York.
  2. Duchateau, L., Janssen, P., Kezic, I. and Fortpied, C. (2003). Evolution of recurrent asthma event rate over time in frailty models. Applied Statistics, 52, 355-363.
  3. Ha, I. D. and Cho, G. H. (2012). H-likelihood approach for variable selection in gamma frailty models. Journal of the Korean Data & Information Science Society, 23, 190-207. https://doi.org/10.7465/jkdi.2012.23.1.199
  4. Ha, I. D. and Noh, M. (2013). A visualizing method for investigating individual frailties using frailtyHL R-package. Journal of the Korean Data & Information Science Society, 24, 931-940. https://doi.org/10.7465/jkdi.2013.24.4.931
  5. Kelly, P. J. and Lim, L. (2000). Survival analysis for recurrent event data: An application to childhood infectious diseases. Statistics in Medicine, 19, 13-33. https://doi.org/10.1002/(SICI)1097-0258(20000115)19:1<13::AID-SIM279>3.0.CO;2-5
  6. Kim, Y. (2013). Survival analysis, Free academy, Seoul.
  7. Kim, Y. (2010). Statistical analysis of recidivism data using frailty effect. The Korean Journal of Applied Statistics, 23, 715-724. https://doi.org/10.5351/KJAS.2010.23.4.715
  8. Kim, Y. (2014). Regression analysis of recurrent events data with incomplete observation gaps. Journal of Applied Statistics, in press.
  9. Klein, J. P. and Moeschberger, M. L. (1997). Survival analysis: Techniques for censored and truncated data, Springer, New York.
  10. McGilchrist, C. A. and Aisbertt, C. W. (1991). Regression with frailty in survival analysis. Biometrics, 47, 461-466. https://doi.org/10.2307/2532138
  11. Nielsen, G. G., Gill, R. D., Andersen, P. K. and Sorensen, T. I. A. (1992). A counting process approach to maximum likelihood estimator in frailty models. Scandinavian Journal of Statistics, 19, 25-43.
  12. Pan, W. (1999). Extending the Iterative convex minorant algorithm to the Cox model for interval-censored data. Journal of Computational and Graphical Statistics, 8, 109-120.
  13. Ripatti, S. and Palmgren, J. (2000). Estimation of multivariate frailty models using penalized partial likelihood. Biometrics, 56, 101-1022.
  14. Sahu, S. K., Dey, D. K., Aslanidou, H. and Sinha, D. (1997). A Weibull regression model with gamma frailties for multivariate survival data. Lifetime Data Analysis, 3, 123-137. https://doi.org/10.1023/A:1009605117713
  15. Sun, J., Kim, Y. J., Hewett, J., Johnson, J. C., Farmer, J. and Gibler, M. (2001). Evaluation of traffic injury prevention programs using counting process approaches. Journal of the American Statistical Association, 96, 469-475. https://doi.org/10.1198/016214501753168181
  16. Therneau, T., Grambsch, P. and Pankratz, V. (2003). Penalized survival models and frailty. Journal of Computational and Graphical Statistics, 12, 156-175. https://doi.org/10.1198/1061860031365
  17. Turnbull, B. W. (1976). The empirical distribution function with arbitrarily grouped censored and truncated data. Journal of the Royal Statistical Society B, 38, 290-295.

Cited by

  1. Variable selection in Poisson HGLMs using h-likelihoood vol.26, pp.6, 2015, https://doi.org/10.7465/jkdi.2015.26.6.1513
  2. Comparison of parametric and nonparametric hazard change-point estimators vol.27, pp.5, 2016, https://doi.org/10.7465/jkdi.2016.27.5.1253
  3. Analysis of recurrent event data with incomplete observation gaps using piecewise models vol.25, pp.5, 2014, https://doi.org/10.7465/jkdi.2014.25.5.1117
  4. Estimation of hazard function and hazard change-point for the rectal cancer data vol.26, pp.6, 2015, https://doi.org/10.7465/jkdi.2015.26.6.1225