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http://dx.doi.org/10.5351/KJAS.2009.22.2.355

Confidence Interval for the Difference or Ratio of Two Median Failure Times from Clustered Survival Data  

Lee, Seung-Yeoun (Department of Applied Mathematics, Sejong University)
Jung, Sin-Ho (Department of Biostatistics and Bioinformatics, Duke University)
Publication Information
The Korean Journal of Applied Statistics / v.22, no.2, 2009 , pp. 355-364 More about this Journal
Abstract
A simple method is proposed for constructing nonparametric confidence intervals for the difference or ratio of two median failure times. The method applies when clustered survival data with censoring is randomized either (I) under cluster randomization or (II) subunit randomization. This method is simple to calculate and is based on non-parametric density estimation. The proposed method is illustrated with the otology study data and HL-A antigen study data. Moreover, the simulation results are reported for practical sample sizes.
Keywords
Censoring; cluster randomization; intracluster correlation; quantile; unit randomization;
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1 Slud, E. V., Byar, D. P. and Green, S. B. (1984). A comparison of reflected versus test-based confidence intervals for the median survival time, based on censored data, Biometrics, 40, 587-600   DOI   ScienceOn
2 Su, J. Q. and Wei, L. J. (1993). Nonparametric estimation for the difference or ratio of median failure times, Biometrics, 49, 603--607   DOI   ScienceOn
3 Teele, D. W., Klein, J. O. and Rosner, B. (1989). Epidemiology of otitis media during the first seven years of life in children in greater Boston: A prospective, cohort study, Journal of Infectious Diseases, 160, 83-94   DOI   ScienceOn
4 Wang, J. L. and Hettmansperger, T. P. (1990). Two-sample inference for median survival times based on one-sample procedures for censored survival data, Journal of the American Statistical Association, 85, 529-536   DOI   ScienceOn
5 Xie, T. and Waksman, J. (2003). Design and sample size estimation in clinical trials with clustered survival times as the primary endpoint, Statistics in Medicine, 22, 2835-2846   DOI   ScienceOn
6 Ying, Z. and Wei, L. J. (1994). The Kaplan-Meier estimate for dependent failure time observations, Journal of Multivariate Analysis, 50, 17-29   DOI   ScienceOn
7 Nelson, W. B. (1969). Hazard plotting for incomplete failure data, Journal of Quality Technology, 1, 27-52   DOI
8 Reid, N. (1981). Estimating the median survival time, Biometrika, 68, 601-608   DOI   ScienceOn
9 Batchelor, J. R. and Hackett, M. (1970). HL-A matching in treatment of burned patients with skin allografts, Lancet, 2, 581-583   DOI   ScienceOn
10 Brookmeyer, R. and Crowley, J. (1982). A confidence interval for the median survival time, Biometrics, 38, 29-41   DOI   ScienceOn
11 Campbell, M. J., Donner, A. and Klar, N. (2007). Developments in cluster randomized trials and Statistics in Medicine, Statistics in Medicine, 26, 2-19   DOI   ScienceOn
12 Efron, B. (1981). Censored data and the bootstrap, Journal of the American Statistical Association, 76, 312-319   DOI   ScienceOn
13 Emerson, J. D. (1982). Nonparametric confidence intervals for the median in the presence ofright censoring, Biometrics, 38, 17-27
14 Le, C. T. (1997). Counting Processes and Survival Analysis, Wiley, New York
15 Howie, V. M. and Schwartz, R. H. (1983). Acute otitis media: One year in general pediatric practice, American Journal of Diseases in Children, 137, 155-158   DOI
16 Hutson, A. D. (2001). Bootstrap-type confidence intervals for quantiles of the survival distribution, Statistics in Medicine, 20, 1693-1702   DOI   ScienceOn
17 Jung, S. H. and Su, J. Q. (1993). Nonparametric estimation for the difference or ratio of median failure times for paired observations, Statistics in Medicine, 14, 275-281   DOI   ScienceOn
18 Moran, P. A. P. (1967). Testing for correlation between non-negative variates, Biometrika, 54, 385-394   DOI