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Comparing Treatments in the Presence of Crossing Survival Curves: An Application to Bone Marrow Transplantation

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  • Brent R. Logan
  • John P. Klein
  • Mei‐Jie Zhang

Abstract

Summary In some clinical studies comparing treatments in terms of their survival curves, researchers may anticipate that the survival curves will cross at some point, leading to interest in a long‐term survival comparison. However, simple comparison of the survival curves at a fixed point may be inefficient, and use of a weighted log‐rank test may be overly sensitive to early differences in survival. We formulate the problem as one of testing for differences in survival curves after a prespecified time point, and propose a variety of techniques for testing this hypothesis. We study these methods using simulation and illustrate them on a study comparing survival for autologous and allogeneic bone marrow transplants.

Suggested Citation

  • Brent R. Logan & John P. Klein & Mei‐Jie Zhang, 2008. "Comparing Treatments in the Presence of Crossing Survival Curves: An Application to Bone Marrow Transplantation," Biometrics, The International Biometric Society, vol. 64(3), pages 733-740, September.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:3:p:733-740
    DOI: 10.1111/j.1541-0420.2007.00975.x
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    References listed on IDEAS

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    1. John P. Klein & Per Kragh Andersen, 2005. "Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function," Biometrics, The International Biometric Society, vol. 61(1), pages 223-229, March.
    2. Per Kragh Andersen, 2003. "Generalised linear models for correlated pseudo-observations, with applications to multi-state models," Biometrika, Biometrika Trust, vol. 90(1), pages 15-27, March.
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    Cited by:

    1. Tunes-da-Silva, Gisela & Klein, John P., 2011. "Cutpoint selection for discretizing a continuous covariate for generalized estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 226-235, January.
    2. Pinar Gunel Karadeniz & Ilker Ercan, 2017. "Examining Tests For Comparing Survival Curves With Right Censored Data," Statistics in Transition New Series, Polish Statistical Association, vol. 18(2), pages 311-328, June.
    3. Kaihuan Qian & Xiaohua Zhou, 2022. "Weighted Log-Rank Test for Clinical Trials with Delayed Treatment Effect Based on a Novel Hazard Function Family," Mathematics, MDPI, vol. 10(15), pages 1-22, July.
    4. Haiming Zhou & Timothy Hanson & Jiajia Zhang, 2017. "Generalized accelerated failure time spatial frailty model for arbitrarily censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(3), pages 495-515, July.
    5. Su, Pei-Fang & Chi, Yunchan & Li, Chung-I & Shyr, Yu & Liao, Yi-De, 2011. "Analyzing survival curves at a fixed point in time for paired and clustered right-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1617-1628, April.
    6. Karadeniz Pinar Gunel & Ercan Ilker, 2017. "Examining Tests for Comparing Survival Curves with Right Censored Data," Statistics in Transition New Series, Polish Statistical Association, vol. 18(2), pages 311-328, June.

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