New methods for the additive hazards model with the informatively interval‐censored failure time data

Abstract

The additive hazards model is one of the most commonly used models for regression analysis of failure time data and many inference procedures have been developed for it under various situations. In particular, Wang et al. (2018a, Computational Statistics and Data Analysis, 125, 1–9) discussed the situation where one observes informatively interval‐censored data and proposed a likelihood estimation approach. However , it involves estimation of the unknown baseline cumulative hazard function and thus may be time‐consuming . Corresponding to this, we propose two new procedures, an estimating equation‐based one and an empirical likelihood‐based one, and both do not need estimation of the cumulative hazard function and can be easily implemented. The asymptotic properties of the proposed methods are established and an extensive simulation study suggests that they work well in practical situations. An application is also provided.

Efficient Estimation of the Additive Risks Model for Interval-Censored Data

“…Some other interesting topics for future research include developing MM-based computationally efficient methods and algorithms for the clustered case-I or case-II interval-censored responses (Huang, 1996;Wang et al, 2022), including exploration of big-data scalability in tune to recent advances via asynchronous distributed EM algorithms (Srivastava et al, 2019). Additionally, developing computationally efficient methods when the inspection time is informative (Zhao et al, 2021) could also be a direction of future research.…”