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A non-parametric test for composite hypotheses in survival analysis

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Summary

For survival data with several concomitant (regressor) variables a large sample non-parametric procedure is presented which provides significance tests of hypotheses about a subset of the concomitant variables. This non-iterative procedure resembles linear model methodology in simplicity and form. The method is useful to eliminate unimportant concomitant variables prior to estimation of model parameters.

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References

  1. Bhapkar, V. P. (1961). A nonparametric test for the problem of several samples,Ann. Math. Statist.,32, 1108–1117.

    Article  MathSciNet  Google Scholar 

  2. Chernoff, H. and Savage, I. R. (1958). Asymptotic normality and efficiency of certain non-parametric test statistics,Ann. Math. Statist.,29, 972–994.

    Article  MathSciNet  Google Scholar 

  3. Cox, D. R. (1972). Regression models in life tables (with discussion),J. R. Statist. Soc., B,34, 187–220.

    MATH  Google Scholar 

  4. Cox, D. R. (1975). Partial likelihood,Biometrika,62, 269–276.

    Article  MathSciNet  Google Scholar 

  5. Crowley, J. (1974). A note on some recent likelihoods leading to the log rank test,Biometrika,61, 533–538.

    Article  MathSciNet  Google Scholar 

  6. Downton, F. (1972). Discussion of Cox's regression models in life tables,J. R. Statist. Soc., B,34, 202–205.

    Google Scholar 

  7. Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance,J. Amer. Statist. Ass.,32, 675–701.

    Article  Google Scholar 

  8. Hájek, J. (1968). Asymptotic normality of simple linear rank statistics under alternatives,Ann. Math. Statist.,39, 325–346.

    Article  MathSciNet  Google Scholar 

  9. Hájek, J. and Sidák, Z. (1967).Theory of Rank Tests, Academic Press, New York.

    MATH  Google Scholar 

  10. Hoeffding, W. (1973). On the centering of a simple linear rank statistic,Ann. Statist.,1, 54–66.

    Article  MathSciNet  Google Scholar 

  11. Kalbfleisch, J. D. and Prentice, R. L. (1973). Marginal likelihoods based on Cox's regression and life model,Biometrika,60, 267–278.

    Article  MathSciNet  Google Scholar 

  12. Kruskal, W. H. (1952). A nonparametric test for the several sample problems,Ann. Math. Statist.,23, 525–540.

    Article  MathSciNet  Google Scholar 

  13. Ogawa, J. (1974).Statistical Theory of the Analysis of Experimental Designs, Marcel Dekker, Inc., New York.

    MATH  Google Scholar 

  14. Puri, M. L. and Sen, P. K. (1973). A note on asymptotic distribution-free tests for subhypotheses in multiple linear regression,Ann. Statist.,1, 553–556.

    Article  MathSciNet  Google Scholar 

  15. Puri, M. L. and Sen, P. K. (1969). A class of rank order tests for a general linear hypothesis,Ann. Math. Statist.,40, 1325–1343.

    Article  MathSciNet  Google Scholar 

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Author notes

  1. H. Dennis Tolley

    Present address: Texas A & M University, Texas, USA

Authors and Affiliations

  1. Radiation Effects Research Foundation, Hiroshima, Japan

    H. Dennis Tolley

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  1. H. Dennis Tolley
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Tolley, H.D. A non-parametric test for composite hypotheses in survival analysis. Ann Inst Stat Math 30, 281–295 (1978). https://doi.org/10.1007/BF02480219

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  • DOI: https://doi.org/10.1007/BF02480219

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