TY - JOUR
T1 - Hypothesis testing in Cox models when continuous covariates are dichotomized
T2 - bias analysis and bootstrap-based test
AU - Sim, Hyunman
AU - Lee, Sungjeong
AU - Kim, Bo Hyung
AU - Shin, Eun
AU - Lee, Woojoo
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Bias analysis
KW - Bootstrap-based test
KW - Cox proportional hazards model
KW - Dichotomization
UR - http://www.scopus.com/inward/record.url?scp=85196674594&partnerID=8YFLogxK
U2 - 10.1007/s00180-024-01520-2
DO - 10.1007/s00180-024-01520-2
M3 - Article
AN - SCOPUS:85196674594
SN - 0943-4062
JO - Computational Statistics
JF - Computational Statistics
ER -