Article
Full entry |
PDF
(0.2 MB)
Feedback
PDF
(0.2 MB)
Feedback
Keywords:
Cox proportional regression model; Breslow method; delayed entry; observation study; mitral valve
Cox proportional regression model; Breslow method; delayed entry; observation study; mitral valve
Summary:
In most clinical studies, patients are observed for extended time periods to evaluate influences in treatment such as drug treatment, approaches to surgery, etc. The primary event in these studies is death, relapse, adverse drug reaction, or development of a new disease. The follow-up time may range from few weeks to many years. Although these studies are long term, the number of observed events is small. Longitudinal studies have increased the importance of statistical methods for time-to event data that can incorporate time-dependent covariates. The Cox proportional regression model is a widely used method. It is a statistical technique for exploring the relationship between the survival of a patient and several explanatory variables. We apply Cox regression models when right censoring and delayed entry survival data are considered. Su and Wang (2012) stated that delayed entry produced biased sample. In the paper we present how re-sampling together with effect of delayed entry affect estimated parameters. The possibilities as well as limitations of this approach are demonstrated through the retrospective study of mitral valve replacement in children under 18 years.
References:
[1] Allison, P. D., SAS Institute: Survival Analysis Using the Sas System: A Practical Guide. SAS Institute Inc., Cary, NC, 1995 http://books.google.cz/books?id=xaBRKg5mtoIC
[2] Breslow, N. E.: Discussion of Professor Cox’s paper. J. Royal Stat. Soc. B 34 (1972), 216–217. MR 0341758
[3] Breslow, N. E.: Covariance analysis of censored survival data. Biometrics 30 (1974), 89–99. DOI 10.2307/2529620
[4] Cassell, D. L.: Don’t Be Loopy: Re-Sampling and Simulation the SAS Way. In: Proceedings of the 2007 SAS Global Forum, SAS Institute Inc., Cary, NC, 2007.
[5] Cary, N. C., SAS Institute Inc.: Users Guide. SAS Institute Inc. SAS/STAT 9.2, Cary, NC, 2008.
[6] Collett, D.: Modeling Survival Data in Medical Research. Chapman & Hall, London, 1994.
[7] Cox, D. R.: Regression models and life tables. Journal of the Royal Statistical Society, Series B 20 (1972), 187–220. MR 0341758 | Zbl 0243.62041
[8] Cox, D. R., Oakes, D.: Analysis of Survival Data. Chapman & Hall, London, 1984. MR 0751780
[9] Edgington, E. S.: Randomization Tests. M. Dekker, New York, 1995. Zbl 0893.62036
[10] Efron, B., Tibshirani, R. J.: An Introduction to the Bootstrap. Chapman & Hall, New York, 1993. MR 1270903 | Zbl 0835.62038
[11] Jun Qian, Bin Li, Ping-yan Chen: Generating Survival Data in the Simulation Studies of Cox Model. In: Information and Computing (ICIC) Third International Conference on, 4 (2010), 93–96.
[12] Kaplan, E. L., Meier, P.: Non-parametric estimation from incomplete observations. J. Am. Stat. Assoc. 53 (1958), 457–481. DOI 10.1080/01621459.1958.10501452 | MR 0093867
[13] Klein, J. P., Moeschberger, M. L.: Survival Analysis: Techniques for Censored and Truncated Data. Springer, New York, 1997. Zbl 0871.62091
[14] Kleinbaum, D. G., Klien, M.: Survival Analysis: A Self-Learning Text. Second Edition, Springer, New York, 2005. MR 2882858
[15] Kongerud, J., Samuelsen, S. O.: A longitudinal study of respiratory symptoms in aluminum potroom workers. American Review of Respiratory Diseases 144 (1991), 10–16. DOI 10.1164/ajrccm/144.1.10
[16] Li Ji: Cox Model Analysis with the Dependently Left Truncated Data. Mathematics Theses, Paper 88, Georgia State University, Atlanta, GA, 2010.
[17] Su Y. R., Wang J. L.: Modeling left-truncated and right-censored survival data with longitudinal covariates. The Annals of Statistics 40, 3 (2012), 1465–1488. DOI 10.1214/12-AOS996 | MR 3015032
Search
Partner of
