A survival dataset describes a set of instances (e.g. patients) and provides,
for each, either the time until an event (e.g. death), or the censoring time
(e.g. when lost to follow-up - which is a lower bound on the time until the
event). We consider the challenge of survival prediction: learning, from such
data, a predictive model that can produce an individual survival distribution
for a novel instance. Many contemporary methods of survival prediction
implicitly assume that the event and censoring distributions are independent
conditional on the instance's covariates - a strong assumption that is
difficult to verify (as we observe only one outcome for each instance) and
which can induce significant bias when it does not hold. This paper presents a
parametric model of survival that extends modern non-linear survival analysis
by relaxing the assumption of conditional independence. On synthetic and
semi-synthetic data, our approach significantly improves estimates of survival
distributions compared to the standard that assumes conditional independence in
the data.

该论文介绍了一种参数化的生存模型，通过放宽条件独立性的假设，扩展现代非线性生存分析并在合成和半合成数据中显著提高了生存分布的估计。