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Testing for center effects on survival and competing risks outcomes using pseudo-value regression

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  • Yanzhi Wang

    (University of Illinois College of Medicine at Peoria)

  • Brent R. Logan

    (Medical College of Wisconsin)

Abstract

In multi-center studies, the presence of a cluster effect leads to correlation among outcomes within a center and requires different techniques to handle such correlation. Testing for a cluster effect can serve as a pre-screening step to help guide the researcher towards the appropriate analysis. With time to event data, score tests have been proposed which test for the presence of a center effect on the hazard function. However, sometimes researchers are interested in directly modeling other quantities such as survival probabilities or cumulative incidence at a fixed time. We propose a test for the presence of a center effect acting directly on the quantity of interest using pseudo-value regression, and derive the asymptotic properties of our proposed test statistic. We examine the performance of our proposed test through simulation studies in both survival and competing risks settings. The proposed test may be more powerful than tests based on the hazard function in settings where the center effect is time-varying. We illustrate the test using a multicenter registry study of survival and competing risks outcomes after hematopoietic cell transplantation.

Suggested Citation

  • Yanzhi Wang & Brent R. Logan, 2019. "Testing for center effects on survival and competing risks outcomes using pseudo-value regression," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 206-228, April.
    • Handle: RePEc:spr:lifeda:v:25:y:2019:i:2:d:10.1007_s10985-018-9443-6
    DOI: 10.1007/s10985-018-9443-6
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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. 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. 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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