Time-to-event outcomes are commonly used as primary endpoints in randomized clinical trials. Despite this, relatively little work incorporates baseline covariate information while also accounting for stratified randomization, a common form of randomization. Moreover, leveraging efficiency gains using these approaches typically requires pre-specifying a subset of covariates that are most predictive of the outcome -- a challenging task in practice, as most trials collect dozens of potentially prognostic baseline variables. In this work, we build on existing literature to propose a data-adaptive and model-robust covariate adjustment method for time-to-event outcomes. Our approach, based on targeted minimum loss-based estimation, allows for data-adaptive covariate selection and model-robust efficient inference on functionals of the survival curve while accounting for stratification. Through extensive simulations and analysis, we showcase the simplicity and improved precision of our method when the covariate set is not known a priori.

翻译：时间-事件结局常作为随机临床试验的主要终点。然而，现有研究在整合基线协变量信息的同时，鲜少考虑分层随机化这一常见随机化形式。此外，利用此类方法提升效率通常需要预先指定对结局最具预测效力的协变量子集——这在实践中颇具挑战，因为多数试验会收集数十个可能具有预后价值的基线变量。本研究在现有文献基础上，提出一种面向时间-事件结局的数据自适应与模型鲁棒协变量调整方法。基于靶向最小损失估计，该方法在考虑分层结构的同时，实现了数据自适应的协变量选择与生存曲线泛函的模型鲁棒高效推断。通过广泛模拟与分析，我们展示了当协变量集合先验未知时，本方法的简洁性与精度的提升。