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Nonparametric Trend Tests for Right-Censored Survival Times

In: Statistical Inference, Econometric Analysis and Matrix Algebra

Author

Listed:
  • Sandra Leissen

    (Technische Universität Dortmund, Fakultät Statistik)

  • Uwe Ligges

    (Technische Universität Dortmund, Fakultät Statistik)

  • Markus Neuhäuser

    (RheinAhr-Campus Remagen, Fachbereich Mathematik und Technik)

  • Ludwig A. Hothorn

    (LeibnizUniversit-annover, Institut fülr Biostatistik)

Abstract

In clinical dose finding studies or preclinical carcinogenesis experiments survival times may arise in groups associated with ordered doses. Here interest may focus on detecting dose dependent trends in the underlying survival functions of the groups. So if a test is to be applied we are faced with an ordered alternative in the test problem, and therefore a trend test may be preferable. Several trend tests for survival data have already been introduced in the literature, e.g., the logrank test for trend, the one by Gehan [4] and Mantel [12], the one by Magel and Degges [11], and the modified ordered logrank test by Liu et al. [10], where the latter is shown to be a special case of the logrank test for trend. Due to their similarity to single contrast tests it is suspected that these tests are more powerful for certain trends than for others. The idea arises whether multiple contrast tests can lead to a better overall power and a more symmetric power over the alternative space. So based on the tests mentioned above two new multiple contrast tests are constructed. In order to compare the conventional with the new tests a simulation study was carried out. The study shows that the new tests preserve the nominal level satisfactory from a certain sample size but fail to conform the expectations in the power improvements.

Suggested Citation

  • Sandra Leissen & Uwe Ligges & Markus Neuhäuser & Ludwig A. Hothorn, 2009. "Nonparametric Trend Tests for Right-Censored Survival Times," Springer Books, in: Bernhard Schipp & Walter Kräer (ed.), Statistical Inference, Econometric Analysis and Matrix Algebra, pages 41-61, Springer.
  • Handle: RePEc:spr:sprchp:978-3-7908-2121-5_3
    DOI: 10.1007/978-3-7908-2121-5_3
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