A Doubly Robust Instrumental Variable Approach for Estimating Average Treatment Effects in Time-to-Event Data with Unmeasured Confounding: Application to Real-World Data on ICU Patients with Septic Shock

Motivated by conflicting conclusions regarding hydrocortisone's treatment effect on ICU patients with vasopressor-dependent septic shock, we developed a novel instrumental variable (IV) estimator to assess the average treatment effect (ATE) in time-to-event data. In real-world data, IV methods are widely used for estimating causal treatment effects in the presence of unmeasured confounding, but existing approaches for time-to-event outcomes are often constrained by strong parametric assumptions and lack desired statistical properties. Based on our derived the efficient influence function (EIF), the proposed estimator possesses double robustness and achieves asymptotic efficiency. It is also flexible to accommodate machine learning models for outcome, treatment, instrument, and censoring for handling complex real-world data. Through extensive simulations, we demonstrate its double robustness, asymptotic normality, and ideal performance in complex data settings. Using electronic health records (EHR) from ICU patients, we identified physician preferences for prescribing hydrocortisone as the IV to evaluate the treatment effect of hydrocortisone on mortality. Our analysis shows no significant benefit or harm of hydrocortisone on these patients. Applying the proposed doubly robust IV method provides reliable estimates in the presence of unmeasured confounders and offers clinicians with valuable evidence-based guidance for prescribing decisions.