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A group sequential test for treatment effect based on the Fine–Gray model

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  • Michael J. Martens
  • Brent R. Logan

Abstract

Competing risks endpoints arise when patients can fail therapy from several causes. Analyzing these outcomes allows one to assess directly the benefit of treatment on a primary cause of failure in a clinical trial setting. Regression models can be used in clinical trials to adjust for residual imbalances in patient characteristics, improving the power to detect treatment differences. But, none of the competing risks methods currently available for use in group sequential trials adjust for covariates. We propose a group sequential test for treatment effect that, because it is based on the Fine–Gray model, permits adjustment for covariates. Our derivations show that its sequence of test statistics has an asymptotic distribution with an independent increments structure, which allows standard techniques such as O'Brien–Fleming designs and error spending functions to be employed to meet type I error rate and power specifications. We demonstrate the test in a reanalysis of BMT CTN 0402, a phase III clinical trial that evaluated an experimental treatment for the prevention of adverse outcomes following blood and marrow transplant. Moreover, using a simulation study of randomized group sequential trials, we demonstrate that the proposed method preserves the type I error rate and power at their nominal levels in the presence of influential covariates.

Suggested Citation

  • Michael J. Martens & Brent R. Logan, 2018. "A group sequential test for treatment effect based on the Fine–Gray model," Biometrics, The International Biometric Society, vol. 74(3), pages 1006-1013, September.
    • Handle: RePEc:bla:biomet:v:74:y:2018:i:3:p:1006-1013
    DOI: 10.1111/biom.12871
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    References listed on IDEAS

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    1. Peng He & Frank Eriksson & Thomas H. Scheike & Mei-Jie Zhang, 2016. "A Proportional Hazards Regression Model for the Subdistribution with Covariates-adjusted Censoring Weight for Competing Risks Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 103-122, March.
    2. 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.
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    Cited by:

    1. Michael J. Martens & Brent R. Logan, 2020. "Group sequential tests for treatment effect on survival and cumulative incidence at a fixed time point," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 603-623, July.
    2. Michael J. Martens & Soyoung Kim & Kwang Woo Ahn, 2023. "Sample size and power determination for multiparameter evaluation in nonlinear regression models with potential stratification," Biometrics, The International Biometric Society, vol. 79(4), pages 3916-3928, December.

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