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Group sequential tests for treatment effect on survival and cumulative incidence at a fixed time point

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  • Michael J. Martens

    (The Emmes Company)

  • Brent R. Logan

    (Medical College of Wisconsin)

Abstract

Medical research frequently involves comparing an event time of interest between treatment groups. Rather than comparing the entire survival or cumulative incidence curves, it is sometimes preferable to evaluate these probabilities at a fixed point in time. Performing a covariate adjusted analysis can improve efficiency, even in randomized clinical trials, but no currently available group sequential test for fixed point analysis provides this adjustment. This paper introduces covariate adjusted group sequential pointwise comparisons of survival and cumulative incidence probabilities. Their test statistics have an asymptotic distribution with independent increments, permitting use of common stopping boundary specification methods. These tests are demonstrated through a redesign of BMT CTN 0402, a clinical trial that evaluated a prophylactic treatment for adverse outcomes following blood and marrow transplantation. A simulation study demonstrates that these tests maintain the type I error rate and power at nominal levels under a variety of settings involving influential covariates.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:lifeda:v:26:y:2020:i:3:d:10.1007_s10985-019-09491-z
    DOI: 10.1007/s10985-019-09491-z
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    References listed on IDEAS

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    1. Min Zhang & Anastasios A. Tsiatis & Marie Davidian, 2008. "Improving Efficiency of Inferences in Randomized Clinical Trials Using Auxiliary Covariates," Biometrics, The International Biometric Society, vol. 64(3), pages 707-715, September.
    2. Thomas H. Scheike & Mei-Jie Zhang & Thomas A. Gerds, 2008. "Predicting cumulative incidence probability by direct binomial regression," Biometrika, Biometrika Trust, vol. 95(1), pages 205-220.
    3. Brent R. Logan & Mei-Jie Zhang & John P. Klein, 2011. "Marginal Models for Clustered Time-to-Event Data with Competing Risks Using Pseudovalues," Biometrics, The International Biometric Society, vol. 67(1), pages 1-7, March.
    4. 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.
    5. 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. Paul Frédéric Blanche & Anders Holt & Thomas Scheike, 2023. "On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 441-482, April.

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