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

Hyunman Sim (), Sungjeong Lee (), Bo-Hyung Kim (), Eun Shin () and Woojoo Lee ()
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Hyunman Sim: Seoul National University
Sungjeong Lee: Celltrion, Inc.
Bo-Hyung Kim: Kyung Hee University Hospital
Eun Shin: Hallym University Dongtan Sacred Heart Hospital
Woojoo Lee: Seoul National University

Computational Statistics, 2025, vol. 40, issue 2, No 13, 907-927

Abstract: 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.

Keywords: Cox proportional hazards model; Dichotomization; Bias analysis; Bootstrap-based test (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-024-01520-2

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