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Hypothesis testing in Cox models when continuous covariates are dichotomized: bias analysis and bootstrap-based test

Author

Listed:
  • Hyunman Sim

    (Seoul National University)

  • Sungjeong Lee

    (Celltrion, Inc.)

  • Bo-Hyung Kim

    (Kyung Hee University Hospital
    Kyung Hee University)

  • Eun Shin

    (Hallym University Dongtan Sacred Heart Hospital)

  • Woojoo Lee

    (Seoul National University)

Abstract

Hypothesis testing for the regression coefficient associated with a dichotomized continuous covariate in a Cox proportional hazards model has been considered in clinical research. Although most existing testing methods do not allow covariates, except for a dichotomized continuous covariate, they have generally been applied. Through an analytic bias analysis and a numerical study, we show that the current practice is not free from an inflated type I error and a loss of power. To overcome this limitation, we develop a bootstrap-based test that allows additional covariates and dichotomizes two-dimensional covariates into a binary variable. In addition, we develop an efficient algorithm to speed up the calculation of the proposed test statistic. Our numerical study demonstrates that the proposed bootstrap-based test maintains the type I error well at the nominal level and exhibits higher power than other methods, as well as that the proposed efficient algorithm reduces computational costs.

Suggested Citation

  • Hyunman Sim & Sungjeong Lee & Bo-Hyung Kim & Eun Shin & Woojoo Lee, 2025. "Hypothesis testing in Cox models when continuous covariates are dichotomized: bias analysis and bootstrap-based test," Computational Statistics, Springer, vol. 40(2), pages 907-927, February.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:2:d:10.1007_s00180-024-01520-2
    DOI: 10.1007/s00180-024-01520-2
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