TY - JOUR
T1 - Conditional independence test of failure and truncation times
T2 - Essential tool for method selection
AU - Ning, Jing
AU - Pak, Daewoo
AU - Zhu, Hong
AU - Qin, Jing
N1 - Funding Information:
The authors thank the editor, associate editor and reviewers for helpful comments and suggestions, which have led to improvements of this paper. This work was supported in part by grants from the National Institutes of Health [R01CA193878 and UL1TR003167]. The authors acknowledge the Texas Advanced Computing Center at The University of Texas at Austin for providing high performance computing resources that have contributed to the research results reported within this paper.
Funding Information:
The authors thank the editor, associate editor and reviewers for helpful comments and suggestions, which have led to improvements of this paper. This work was supported in part by grants from the National Institutes of Health [ R01CA193878 and UL1TR003167 ]. The authors acknowledge the Texas Advanced Computing Center at The University of Texas at Austin for providing high performance computing resources that have contributed to the research results reported within this paper.
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2022/4
Y1 - 2022/4
N2 - Conditional independence assumption of truncation and failure times conditioning on covariates is a fundamental and common assumption in the regression analysis of left-truncated and right-censored data. Testing for this assumption is essential to ensure the correct inference on the failure time, but this has often been overlooked in the literature. With consideration of challenges caused by left truncation and right censoring, tests for this conditional independence assumption are developed in which the generalized odds ratio derived from a Cox proportional hazards model on the failure time and the concept of Kendall's tau are combined. Except for the Cox proportional hazards model, no additional model assumptions are imposed, and the distributions of the truncation time and conditioning variables are unspecified. The asymptotic properties of the test statistic are established and an easy implementation for obtaining its distribution is developed. The performance of the proposed test has been evaluated through simulation studies and two real studies.
AB - Conditional independence assumption of truncation and failure times conditioning on covariates is a fundamental and common assumption in the regression analysis of left-truncated and right-censored data. Testing for this assumption is essential to ensure the correct inference on the failure time, but this has often been overlooked in the literature. With consideration of challenges caused by left truncation and right censoring, tests for this conditional independence assumption are developed in which the generalized odds ratio derived from a Cox proportional hazards model on the failure time and the concept of Kendall's tau are combined. Except for the Cox proportional hazards model, no additional model assumptions are imposed, and the distributions of the truncation time and conditioning variables are unspecified. The asymptotic properties of the test statistic are established and an easy implementation for obtaining its distribution is developed. The performance of the proposed test has been evaluated through simulation studies and two real studies.
KW - Conditional generalized odds ratio
KW - Conditional independence
KW - Cox proportional hazards model
KW - Left truncation
KW - Right censoring
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U2 - 10.1016/j.csda.2021.107402
DO - 10.1016/j.csda.2021.107402
M3 - Article
C2 - 34898770
AN - SCOPUS:85119682318
SN - 0167-9473
VL - 168
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
M1 - 107402
ER -