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Covariate-adjusted quantile inference with competing risks

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  • Lee, Minjung
  • Han, Junhee

Abstract

Quantile inference with adjustment for covariates has not been widely investigated on competing risks data. We propose covariate-adjusted quantile inferences based on the cause-specific proportional hazards regression of the cumulative incidence function. We develop the construction of confidence intervals for quantiles of the cumulative incidence function given a value of covariates and for the difference of quantiles based on the cumulative incidence functions between two treatment groups with common covariates. Simulation studies show that the procedures perform well. We illustrate the proposed methods using early stage breast cancer data.

Suggested Citation

  • Lee, Minjung & Han, Junhee, 2016. "Covariate-adjusted quantile inference with competing risks," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 57-63.
    • Handle: RePEc:eee:csdana:v:101:y:2016:i:c:p:57-63
    DOI: 10.1016/j.csda.2016.02.012
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    References listed on IDEAS

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    1. Ruosha Li & Limin Peng, 2015. "Quantile regression adjusting for dependent censoring from semicompeting risks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(1), pages 107-130, January.
    2. L. Peng & J. P. Fine, 2007. "Nonparametric quantile inference with competing–risks data," Biometrika, Biometrika Trust, vol. 94(3), pages 735-744.
    3. Peng, Limin & Fine, Jason P., 2009. "Competing Risks Quantile Regression," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1440-1453.
    4. J.-H. Jeong & J. P. Fine, 2009. "A note on cause-specific residual life," Biometrika, Biometrika Trust, vol. 96(1), pages 237-242.
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