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Title: Study of Bootstrap Estimates in Cox Regression Model with Delayed Entry (English)
Author: Bělašková, Silvie
Author: Fišerová, Eva
Author: Krupičková, Sylvia
Language: English
Journal: Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
ISSN: 0231-9721
Volume: 52
Issue: 2
Year: 2013
Pages: 21-30
Summary lang: English
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Category: math
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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. (English)
Keyword: Cox proportional regression model
Keyword: Breslow method
Keyword: delayed entry
Keyword: observation study
Keyword: mitral valve
MSC: 62G09
MSC: 62N01
MSC: 62N02
MSC: 62P10
idZBL: Zbl 06296011
idMR: MR3202376
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Date available: 2013-12-18T15:19:59Z
Last updated: 2014-07-30
Stable URL: http://hdl.handle.net/10338.dmlcz/143535
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Reference: [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.
Reference: [2] Breslow, N. E.: Discussion of Professor Cox’s paper. J. Royal Stat. Soc. B 34 (1972), 216–217. MR 0341758
Reference: [3] Breslow, N. E.: Covariance analysis of censored survival data. Biometrics 30 (1974), 89–99. 10.2307/2529620
Reference: [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.
Reference: [5] Cary, N. C., SAS Institute Inc.: User s Guide. SAS Institute Inc. SAS/STAT 9.2, Cary, NC, 2008.
Reference: [6] Collett, D.: Modeling Survival Data in Medical Research. Chapman & Hall, London, 1994.
Reference: [7] Cox, D. R.: Regression models and life tables. Journal of the Royal Statistical Society, Series B 20 (1972), 187–220. Zbl 0243.62041, MR 0341758
Reference: [8] Cox, D. R., Oakes, D.: Analysis of Survival Data. Chapman & Hall, London, 1984. MR 0751780
Reference: [9] Edgington, E. S.: Randomization Tests. M. Dekker, New York, 1995. Zbl 0893.62036
Reference: [10] Efron, B., Tibshirani, R. J.: An Introduction to the Bootstrap. Chapman & Hall, New York, 1993. Zbl 0835.62038, MR 1270903
Reference: [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.
Reference: [12] Kaplan, E. L., Meier, P.: Non-parametric estimation from incomplete observations. J. Am. Stat. Assoc. 53 (1958), 457–481. MR 0093867, 10.1080/01621459.1958.10501452
Reference: [13] Klein, J. P., Moeschberger, M. L.: Survival Analysis: Techniques for Censored and Truncated Data. Springer, New York, 1997. Zbl 0871.62091
Reference: [14] Kleinbaum, D. G., Klien, M.: Survival Analysis: A Self-Learning Text. Second Edition, Springer, New York, 2005. MR 2882858
Reference: [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. 10.1164/ajrccm/144.1.10
Reference: [16] Li Ji: Cox Model Analysis with the Dependently Left Truncated Data. Mathematics Theses, Paper 88, Georgia State University, Atlanta, GA, 2010.
Reference: [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. MR 3015032, 10.1214/12-AOS996
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