Application of discrete-time survival methods for continuous-time survival
prediction is considered. For this purpose, a scheme for discretization of
continuous-time data is proposed by considering the quantiles of the estimated
event-time distribution, and, for smaller data sets, it is found to be
preferable over the commonly used equidistant scheme. Furthermore, two
interpolation schemes for continuous-time survival estimates are explored, both
of which are shown to yield improved performance compared to the discrete-time
estimates. The survival methods considered are based on the likelihood for
right-censored survival data, and parameterize either the probability mass
function (PMF) or the discrete-time hazard rate, both with neural networks.
Through simulations and study of real-world data, the hazard rate
parametrization is found to perform slightly better than the parametrization of
the PMF. Inspired by these investigations, a continuous-time method is proposed
by assuming that the continuous-time hazard rate is piecewise constant. The
method, named PC-Hazard, is found to be highly competitive with the
aforementioned methods in addition to other methods for survival prediction
found in the literature.

本文提出了一种应用离散时间生存方法进行连续时间生存预测的方法，并探讨了 PMF 和离散时间风险率的参数化两种神经网络插值方法，并通过真实和模拟数据研究表明离散时间风险率参数化方法略胜一筹。同时，也提出了一种基于假设连续时间风险率是分段恒定的连续时间方法，并发现该方法在生存预测方面与其他方法相比非常有竞争力。