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.
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Data availability
The dataset gbsg is available from the R package survival (Therneau 2024).
Code availability
The R-package DTCox is freely available from https://sites.google.com/view/lwj221, and the program code used for real data analysis is provided on the same website.
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Funding
This work was supported by the National Research Foundation of Korea (BK21 Center for Integrative Response to Health Disasters, Graduate School of Public Health, Seoul National University)(NO.419 999 0514025).
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Sim, H., Lee, S., Kim, BH. et al. Hypothesis testing in Cox models when continuous covariates are dichotomized: bias analysis and bootstrap-based test. Comput Stat 40, 907–927 (2025). https://doi.org/10.1007/s00180-024-01520-2
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DOI: https://doi.org/10.1007/s00180-024-01520-2