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The Inverse Log-Rank Test: A Versatile Procedure for Late Separating Survival Curves

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  • Jimmy T. Efird

    (VA Cooperative Studies Program Coordinating Center, Boston, MA 02111, USA
    School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA)

Abstract

Often in the planning phase of a clinical trial, a researcher will need to choose between a standard versus weighted log-rank test (LRT) for investigating right-censored survival data. While a standard LRT is optimal for analyzing evenly distributed but distinct survival events (proportional hazards), an appropriately weighted LRT test may be better suited for handling non-proportional, delayed treatment effects. The “a priori” misspecification of this alternative may result in a substantial loss of power when determining the effectiveness of an experimental drug. In this paper, the standard unweighted and inverse log-rank tests (iLRTs) are compared with the multiple weight, default Max-Combo procedure for analyzing differential late survival outcomes. Unlike combination LRTs that depend on the arbitrary selection of weights, the iLRT by definition is a single weight test and does not require implicit multiplicity correction. Empirically, both weighted methods have reasonable flexibility for assessing continuous survival curve differences from the onset of a study. However, the iLRT may be preferable for accommodating delayed separating survival curves, especially when one arm finishes first. Using standard large-sample methods, the power and sample size for the iLRT are easily estimated without resorting to complex and timely simulations.

Suggested Citation

  • Jimmy T. Efird, 2023. "The Inverse Log-Rank Test: A Versatile Procedure for Late Separating Survival Curves," IJERPH, MDPI, vol. 20(24), pages 1-23, December.
  • Handle: RePEc:gam:jijerp:v:20:y:2023:i:24:p:7164-:d:1297943
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    References listed on IDEAS

    as
    1. Song Yang & Ross Prentice, 2010. "Improved Logrank-Type Tests for Survival Data Using Adaptive Weights," Biometrics, The International Biometric Society, vol. 66(1), pages 30-38, March.
    2. Lee, Seung-Hwan, 2007. "On the versatility of the combination of the weighted log-rank statistics," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6557-6564, August.
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