To draw real-world evidence about the comparative effectiveness of multiple time-varying treatments on patient survival, we develop a joint marginal structural survival model and a novel weighting strategy to account for time-varying confounding and censoring. Our methods formulate complex longitudinal treatments with multiple start/stop switches as the recurrent events with discontinuous intervals of treatment eligibility. We derive the weights in continuous time to handle a complex longitudinal dataset without the need to discretize or artificially align the measurement times. We further use machine learning models designed for censored survival data with time-varying covariates and the kernel function estimator of the baseline intensity to efficiently estimate the continuous-time weights. Our simulations demonstrate that the proposed methods provide better bias reduction and nominal coverage probability when analyzing observational longitudinal survival data with irregularly spaced time intervals, compared to conventional methods that require aligned measurement time points. We apply the proposed methods to a large-scale COVID-19 dataset to estimate the causal effects of several COVID-19 treatments on the composite of in-hospital mortality and ICU admission.

翻译：为了从真实世界证据中评估多种时变治疗对患者生存期的相对有效性，我们构建了一个联合边际结构生存模型，并提出了一种新型加权策略，以控制时变混杂因素和删失。该方法将包含多次启动/终止切换的复杂纵向治疗过程，建模为具有不连续治疗资格区间的复发事件。我们推导了连续时间权重，无需对测量时间进行离散化或人为对齐即可处理复杂纵向数据集。进一步地，我们采用专为处理时变协变量的删失生存数据设计的机器学习模型，以及基线强度的核函数估计器，以高效估计连续时间权重。仿真结果表明：在处理具有不规则采样间隔的观察性纵向生存数据时，相较于需要对齐测量时间点的传统方法，所提方法能更有效地降低偏差并实现名义覆盖概率。我们将该方法应用于大规模COVID-19数据集，以估计多种COVID-19治疗对院内死亡与重症监护综合结局的因果效应。