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, Woojoo Lee

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
JournalComputational Statistics
DOIs
Publication statusAccepted/In press - 2024

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

Keywords

  • Bias analysis
  • Bootstrap-based test
  • Cox proportional hazards model
  • Dichotomization

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