A0342
Title: A continuous-time joint marginal structural survival model for causal inferences about multiple intermittent treatments
Authors: Liangyuan Hu - Rutgers University (United States) [presenting]
Himanshu Joshi - Mount Sinai (United States)
Erick Scott - C Structure (United States)
Fan Li - Yale University (United States)
Abstract: To draw real-world evidence about the comparative effectiveness of multiple time-varying treatments on patient survival, a joint marginal structural survival model and a novel weighting strategy are developed to account for time-varying confounding and censoring. The methods formulate complex longitudinal treatments with multiple start/stop switches as the recurrent events with discontinuous intervals of treatment eligibility. The weights are derived in continuous time to handle a complex longitudinal dataset without the need to discretize or artificially align the measurement times. Machine learning models, designed for censored survival data with time-varying covariates and the kernel function estimator of the baseline intensity, are further used to efficiently estimate the continuous-time weights. 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. The proposed methods are applied 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
