Kernel Machines for Current Status Data
This work addresses survival analysis for current status data, offering a flexible estimation method that is incremental in improving performance for this specific domain.
The authors tackled the problem of estimating failure time distributions from current status data, a type of censored survival data, by proposing a kernel machine approach that minimizes a regularized empirical risk with a new loss function. They proved theoretical convergence and showed empirically that their method is comparable to or better than state-of-the-art approaches in simulations and real-world data.
In survival analysis, estimating the failure time distribution is an important and difficult task, since usually the data is subject to censoring. Specifically, in this paper we consider current status data, a type of data where all of the observations are censored. The format of the data is such that the failure time is restricted to knowledge of whether or not the failure time exceeds a random monitoring time. We propose a flexible kernel machine approach for estimation of the failure time expectation as a function of the covariates, with current status data. In order to obtain the kernel machine decision function, we minimize a regularized version of the empirical risk with respect to a new loss function. Using finite sample bounds and novel oracle inequalities, we prove that the obtained estimator converges to the true conditional expectation for a large family of probability measures. Finally, we present a simulation study and an analysis of real-world data that compares the performance of the proposed approach to existing methods. We show empirically that our approach is comparable to current state of the art, and in some cases is even better.