@article {16718,
title = {Estimating a Treatment Effect in Residual Time Quantiles under the Additive Hazards Model.},
journal = {Statistics in biosciences},
volume = {9},
year = {2017},
month = {2017 Jun},
pages = {298-315},
abstract = {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{\textquoteright}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{\textquoteright}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 translates 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.},
issn = {1867-1764},
doi = {10.1371/journal.pgen.1006850},
author = {Crouch, Luis Alexander and Zheng, Cheng and Chen, Ying Qing}
}