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:除了传统的参数和半参数方法(例如,基于Cox成比例危害模型)外,还开发了灵活的机器学习方法来估计有条件生存功能,然而,其中许多方法要么隐含,要么明确针对风险分级,而不是总体生存功能。其他方法则仅适用于离散时间设置或要求反向审查重量的概率,这可能与结果生存功能本身一样难以估计。这里,我们采用有条件生存功能的分解法,采用可观察回归模型,这种模型审查与脱轨不起作用。这样可以应用一系列灵活的回归和分类方法来估计有条件生存功能,而不仅仅是明确处理生存数据所固有的复杂性的方法。我们根据这种分解状态、实证性评估其性能并展示其在艾滋病毒疫苗试验数据上的使用情况。</s>