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Testing Quasi-independence for Truncation Data

Author

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  • Emura, Takeshi
  • Wang, Weijing

Abstract

Quasi-independence is a common assumption for analyzing truncated data. To verify this condition, we propose a class of weighted log-rank type statistics that includes existing tests proposed by Tsai (1990) and Martin and Betensky (2005) as special cases. To choose an appropriate weight function that may lead to a more power test, we derive a score test when the dependence structure under the alternative hypothesis is modeled via the odds ratio function proposed by Chaieb, Rivest and Abdous (2006). Asymptotic properties of the proposed tests are established based on the functional delta method which can handle more general situations than results based on rank-statistics or U-statistics. Extension of the proposed methodology under two different censoring settings is also discussed. Simulations are performed to examine finite-sample performances of the proposed method and its competitors. Two datasets are analyzed for illustrative purposes.

Suggested Citation

  • Emura, Takeshi & Wang, Weijing, 2009. "Testing Quasi-independence for Truncation Data," MPRA Paper 58582, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:58582
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    File URL: https://mpra.ub.uni-muenchen.de/58582/1/MPRA_paper_58582.pdf
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    References listed on IDEAS

    as
    1. Ding, A. Adam & Wang, Weijing, 2004. "Testing Independence for Bivariate Current Status Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 145-155, January.
    2. Martin, Emily C. & Betensky, Rebecca A., 2005. "Testing Quasi-Independence of Failure and Truncation Times via Conditional Kendall's Tau," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 484-492, June.
    3. Lajmi Lakhal Chaieb & Louis-Paul Rivest & Belkacem Abdous, 2006. "Estimating survival under a dependent truncation," Biometrika, Biometrika Trust, vol. 93(3), pages 655-669, September.
    4. Emura, Takeshi & Wang, Weijing, 2010. "Testing quasi-independence for truncation data," Journal of Multivariate Analysis, Elsevier, vol. 101(1), pages 223-239, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Conditional likelihood; Kendall’s tau; Mantel-Heanszel test; Power; Right-censoring; Survival data; Two-by-two table;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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