C-index regression for recurrent event data

Recurrent event data analysis plays an important role in many fields, e.g., medicine, social science, and economics. While the existing approaches under the proportional rates or mean model yield poor performance when the underlying model is misspecified, we propose a novel model-free approach by introducing a lower bound on the concordance index (C-Index). We develop an estimation method through deriving a continuous lower bound on the C-Index based on the log-sigmoid function and also provide a variable selection procedure in high dimensional settings. Under both low and high dimensional settings, simulation results show that the proposed methods outperform the gamma frailty recurrent event model when the proportional mean assumption is violated. Moreover, an application to the hospital readmission dataset shows results in line with previous studies and a higher C-Index value further assures model decency.

Automated summary

Recurrent event data analysis is crucial in various fields, and a novel model-free approach was proposed in this study, which introduces a lower bound on the concordance index to improve performance and provide a variable selection procedure in high dimensional settings. Simulation results demonstrate that the proposed methods outperform traditional models when the proportional mean assumption is violated, and real-world application results are consistent with prior studies.