TY - JOUR
T1 - Estimating a Treatment Effect in Residual Time Quantiles Under the Additive Hazards Model
AU - Crouch, Luis Alexander
AU - Zheng, Cheng
AU - Chen, Ying Qing
N1 - Funding Information:
This work was partly supported by NIH Grants R01 AI089341, R01 CA172415, R01 MH105857, R01 AI121259, and UM1 AI068617. The authors thank the Editor for the helpful comments that have improved the quality of this paper.
Publisher Copyright:
© 2016, International Chinese Statistical Association.
PY - 2017/6/1
Y1 - 2017/6/1
N2 - For randomized clinical trials where the endpoint of interest is a time-to-event subject to censoring, estimating the treatment effect has mostly focused on the hazard ratio from the Cox proportional hazards model. Since the model’s proportional hazards assumption is not always satisfied, a useful alternative, the so-called additive hazards model, may instead be used to estimate a treatment effect on the difference of hazard functions. Still, the hazards difference may be difficult to grasp intuitively, particularly in a clinical setting of, e.g., patient counseling, or resource planning. In this paper, we study the quantiles of a covariate’s conditional survival function in the additive hazards model. Specifically, we estimate the residual time quantiles, i.e., the quantiles of survival times remaining at a given time t, conditional on the survival times greater than t, for a specific covariate in the additive hazards model. We use the estimates to translate the hazards difference into the difference in residual time quantiles, which allows a more direct clinical interpretation. We determine the asymptotic properties, assess the performance via Monte-Carlo simulations, and demonstrate the use of residual time quantiles in two real randomized clinical trials.
AB - For randomized clinical trials where the endpoint of interest is a time-to-event subject to censoring, estimating the treatment effect has mostly focused on the hazard ratio from the Cox proportional hazards model. Since the model’s proportional hazards assumption is not always satisfied, a useful alternative, the so-called additive hazards model, may instead be used to estimate a treatment effect on the difference of hazard functions. Still, the hazards difference may be difficult to grasp intuitively, particularly in a clinical setting of, e.g., patient counseling, or resource planning. In this paper, we study the quantiles of a covariate’s conditional survival function in the additive hazards model. Specifically, we estimate the residual time quantiles, i.e., the quantiles of survival times remaining at a given time t, conditional on the survival times greater than t, for a specific covariate in the additive hazards model. We use the estimates to translate the hazards difference into the difference in residual time quantiles, which allows a more direct clinical interpretation. We determine the asymptotic properties, assess the performance via Monte-Carlo simulations, and demonstrate the use of residual time quantiles in two real randomized clinical trials.
KW - Clinical trial
KW - Covariate-specific estimate
KW - Hazard function
KW - Remaining time
KW - Survival analysis
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U2 - 10.1007/s12561-016-9180-x
DO - 10.1007/s12561-016-9180-x
M3 - Article
C2 - 28694879
AN - SCOPUS:84992694452
VL - 9
SP - 298
EP - 315
JO - Statistics in Biosciences
JF - Statistics in Biosciences
SN - 1867-1764
IS - 1
ER -