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A weighted log-rank test for comparing two survival curves

Author

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  • Lee Seung-Hwan

    (Department of Mathematics, Illinois Wesleyan University, Bloomington, Illinois 61701, USA)

  • Lee Eun-Joo

    (Department of Mathematics and Computational Sciences, Millikin University, Decatur, Illinois 62522, USA)

Abstract

This paper proposes a weighted log-rank test that maintains sensitivity to realistic alternatives of two survival curves, such as crossing curves, in the presence of heavy censoring. The new test incorporates a weight function that changes over the censoring level, increasing adaptivity and flexibility of the commonly used weighted log-rank tests. The new statistic is asymptotically normal under the null hypothesis that there is no difference in survival between two groups. The performances of the new test are evaluated via simulations under both proportional and non-proportional alternatives. We illustrate the new method with a real-world application.

Suggested Citation

  • Lee Seung-Hwan & Lee Eun-Joo, 2020. "A weighted log-rank test for comparing two survival curves," Monte Carlo Methods and Applications, De Gruyter, vol. 26(3), pages 253-262, September.
    • Handle: RePEc:bpj:mcmeap:v:26:y:2020:i:3:p:253-262:n:1
    DOI: 10.1515/mcma-2020-2064
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    References listed on IDEAS

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    1. R.D. Gill, 1980. "Censoring and Stochastic Integrals," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 34(2), pages 124-124, June.
    2. Lang Wu & Peter B. Gilbert, 2002. "Flexible Weighted Log-Rank Tests Optimal for Detecting Early and/or Late Survival Differences," Biometrics, The International Biometric Society, vol. 58(4), pages 997-1004, December.
    3. Yu Shen & Jianwen Cai, 2001. "Maximum of the Weighted Kaplan-Meier Tests with Application to Cancer Prevention and Screening Trials," Biometrics, The International Biometric Society, vol. 57(3), pages 837-843, September.
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