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Joint Inference for Competing Risks Survival Data

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  • Gang Li
  • Qing Yang

Abstract

This article develops joint inferential methods for the cause-specific hazard function and the cumulative incidence function of a specific type of failure to assess the effects of a variable on the time to the type of failure of interest in the presence of competing risks. Joint inference for the two functions are needed in practice because (i) they describe different characteristics of a given type of failure, (ii) they do not uniquely determine each other, and (iii) the effects of a variable on the two functions can be different and one often does not know which effects are to be expected. We study both the group comparison problem and the regression problem. We also discuss joint inference for other related functions. Our simulation shows that our joint tests can be considerably more powerful than the Bonferroni method, which has important practical implications to the analysis and design of clinical studies with competing risks data. We illustrate our method using a Hodgkin disease data and a lymphoma data. Supplementary materials for this article are available online.

Suggested Citation

  • Gang Li & Qing Yang, 2016. "Joint Inference for Competing Risks Survival Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1289-1300, July.
    • Handle: RePEc:taf:jnlasa:v:111:y:2016:i:515:p:1289-1300
    DOI: 10.1080/01621459.2015.1093942
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    1. Håkan Lindkvist & Yuri Belyaev, 1998. "A Class of Non‐parametric Tests in the Competing Risks Model for Comparing Two Samples," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 25(1), pages 143-150, March.
    2. Bajorunaite, Ruta & Klein, John P., 2007. "Two-sample tests of the equality of two cumulative incidence functions," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4269-4281, May.
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    4. J. P. Fine, 1999. "Analysing competing risks data with transformation models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 817-830.
    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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