The conditional survival function of a time-to-event outcome subject to censoring and truncation is a common target of estimation in survival analysis. This parameter may be of scientific interest and also often appears as a nuisance in nonparametric and semiparametric problems. In addition to classical parametric and semiparametric methods (e.g., based on the Cox proportional hazards model), flexible machine learning approaches have been developed to estimate the conditional survival function. However, many of these methods are either implicitly or explicitly targeted toward risk stratification rather than overall survival function estimation. Others apply only to discrete-time settings or require inverse probability of censoring weights, which can be as difficult to estimate as the outcome survival function itself. Here, we employ a decomposition of the conditional survival function in terms of observable regression models in which censoring and truncation play no role. This allows application of an array of flexible regression and classification methods rather than only approaches that explicitly handle the complexities inherent to survival data. We outline estimation procedures based on this decomposition, empirically assess their performance, and demonstrate their use on data from an HIV vaccine trial.

翻译：在删失和截断条件下，时间至事件结局的条件生存函数是生存分析中常见的估计目标。该参数不仅具有科学意义，还常作为非参数和半参数问题中的冗余参数出现。除经典参数和半参数方法（如基于Cox比例风险模型的方法）外，灵活机器学习方法也被开发用于估计条件生存函数。然而，这些方法中许多隐式或显式地针对风险分层而非整体生存函数估计设计。其他方法仅适用于离散时间设置，或需要逆概率删失权重，而后者可能和结局生存函数本身一样难以估计。本文采用条件生存函数关于可观测回归模型的分解形式，其中删失和截断不起作用。这使得可以应用一系列灵活回归和分类方法，而非仅限于显式处理生存数据固有复杂性的方法。我们概述了基于此分解的估计流程，通过实证评估其性能，并利用HIV疫苗试验数据展示了其应用。