Regression analysis of informative current status data with the additive hazards model

详细信息    查看全文

• 作者：Shishun Zhao (1)
  Tao Hu (2)
  Ling Ma (3)
  Peijie Wang (1)
  Jianguo Sun (3)
  
  1. College of Mathematics ; Jilin University ; Changchun ; 130012 ; People鈥檚 Republic of China
  2. School of Mathematical Sciences and BCMIIS ; Capital Normal University ; Beijing ; 100048 ; People鈥檚 Republic of China
  3. Department of Statistics ; University of Missouri ; 146 Middlebush Hall ; MO ; 65211 ; USA
  
• 关键词：Additive hazards model ; Current status data ; Efficient estimation ; Informative censoring
• 刊名：Lifetime Data Analysis
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：21
• 期：2
• 页码：241-258
• 全文大小：214 KB
• 参考文献：1. Bickel, PJ, Kwon, J (2001) Inference for semiparametric models: some questions and an answer. Stat Sin 11: pp. 863-960
  2. Chen, X, Fan, Y, Tsyrennikov, V (2006) Efficient estimation of semiparametric multivariate copula models. J Am Stat Assoc 101: pp. 1228-1240 CrossRef
  3. Chen, L, Sun, J (2009) A multiple imputation approach to the analysis of current status data with the additive hazards model. Commun Stat Theory Methods 38: pp. 1009-1018 CrossRef
  4. Chen, MH, Tong, X, Sun, J (2009) A frailty model approach for regression analysis of multivariate current status data. Stat Med 28: pp. 3424-3426 CrossRef
  5. Chen, CM, Lu, TFC, Chen, MH, Hsu, CM (2012) Semiparametric transformation models for current status data with informative censoring. Biom J 54: pp. 641-656 CrossRef
  6. Chen DGD, Sun J, Peace K (2012b) Interval-censored time-to-event data: methods and applications. Chapman & Hall/CRC
  7. Hougaard, P (2000) Analysis of multivariate survival data. Springer, New York CrossRef
  8. Huang, X, Wolfe, RA (2002) A frailty model for informative censoring. Biometrics 58: pp. 510-520 CrossRef
  9. Kalbfleisch, JD, Prentice, RL (2002) The statistical analysis of failure time data. Wiley, New York CrossRef
  10. Keiding, N (1991) Age-specific incidence and prevalence: a statistical perspective (with discussion). J R Stat Soc A 154: pp. 371-412 CrossRef
  11. Kulich, M, Lin, DY (2000) Additive hazards regression with covariate measurement error. J Am Stat Assoc 95: pp. 238-248 CrossRef
  12. Lagakos, SW, Louis, TA (1988) Use of tumor lethality to interpret tumorgenicity experiments lacking causing-of-death data. Appl Stat 37: pp. 169-179 CrossRef
  13. Li, J, Wang, C, Sun, J (2012) Regression analysis of clustered interval-censored failure time data with the additive hazards model. J Nonparametr Stat 24: pp. 1041-1050 CrossRef
  14. Lin, DY, Ying, Z (1994) Semiparametric analysis of the additive risk model. Biometrika 81: pp. 61-71 CrossRef
  15. Lin, DY, Oakes, D, Ying, Z (1998) Additive hazards regression with current status data. Biometrika 85: pp. 289-298 CrossRef
  16. Lu, M, Zhang, Y, Huang, J (2007) Estimation of the mean function with panel count data using monotone polynomial splines. Biometrika 94: pp. 705-718 CrossRef
  17. Martinussen, T, Scheike, TH (2002) Efficient estimation in additive hazards regression with current status data. Biometrika 89: pp. 649-658 CrossRef
  18. Nelsen, RB (2006) An introduction to copulas. Springer, New York
  19. Pollard, D (1984) Convergence of stochastic processes. Springer, New York CrossRef
  20. Ramsay, JO (1988) Monotone regression splines in action. Stat Sci 3: pp. 425-441 CrossRef
  21. Schumaker, LL (1981) Spline functions: basic theory. Wiley, New York
  22. Shen, X, Wong, WH (1994) Convergence rate of sieve estimates. Ann Stat 22: pp. 580-615 CrossRef
  23. Shorack, GR (2000) Probability for statisticians. Springer, New York
  24. Shen, X (1997) On methods of sieves and penalization. Ann Stat 25: pp. 2555-2591 CrossRef
  25. Sun, J (2006) The statistical analysis of interval-censored failure time data. Springer, New York
  26. Sun, L, Park, D, Sun, J (2006) The additive hazards model for recurrent gap times. Stat Sin 16: pp. 919-932
  27. Titman AC (2013) A pool-adjacent-violators type algorithm for non-parametric estimation of current status data with dependent censoring. Lifetime Data Anal. Published Online: 22 June 2013
  28. Tong, X, Hu, T, Sun, J (2012) Efficient estimation for additive hazards regression with bivariate current status data. Sci China Math 55: pp. 763-774 CrossRef
  29. Vaart, AW, Wellner, JA (1996) Weak convergence and empirical processes. Springer, New York CrossRef
  30. Wang, L, Sun, J, Tong, X (2010) Regression analysis of case II interval-censored failure time data with the additive hazards model. Stat Sin 20: pp. 1709-1723
  31. Wang, C, Sun, J, Sun, L, Zhou, J, Wang, D (2012) Nonparametric estimation of current status data with dependent censoring. Lifetime Data Anal 18: pp. 434-445 CrossRef
  32. Zhang, Z, Sun, J, Sun, L (2005) Statistical analysis of current status data with informative observation times. Stat Med 24: pp. 1399-1407 CrossRef
  33. Zheng, M, Klein, JP (1995) Estimates of marginal survival for dependent competing risk based on an assumed copula. Biometrika 82: pp. 127-138 CrossRef
  34. Zhou, X, Sun, L (2003) Additive hazards regression with missing censoring information. Stat Sin 13: pp. 1237-1258
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics
  Statistics
  Statistics for Life Sciences, Medicine and Health Sciences
  Quality Control, Reliability, Safety and Risk
  Statistics for Business, Economics, Mathematical Finance and Insurance
  Operation Research and Decision Theory
  
• 出版者：Springer Netherlands
• ISSN：1572-9249

文摘

This paper discusses regression analysis of current status failure time data arising from the additive hazards model in the presence of informative censoring. Many methods have been developed for regression analysis of current status data under various regression models if the censoring is noninformative, and also there exists a large literature on parametric analysis of informative current status data in the context of tumorgenicity experiments. In this paper, a semiparametric maximum likelihood estimation procedure is presented and in the method, the copula model is employed to describe the relationship between the failure time of interest and the censoring time. Furthermore, I-splines are used to approximate the nonparametric functions involved and the asymptotic consistency and normality of the proposed estimators are established. A simulation study is conducted and indicates that the proposed approach works well for practical situations. An illustrative example is also provided.