Reducing unmeasured confounding from non-compliance with randomization in surgical RCTs: a simulation study

Non-compliance with randomization is pervasive in surgical randomized controlled trials (RCTs), introducing unmeasured confounding that undermines valid causal inference. Conventional methods, such as intention-to-treat (ITT), per-protocol (PP), as-treated (AT) and instrumental variable (IV) analysis, exhibit significant limitations. We conducted a simulation study to compare the performance of negative control methods (NCM), including difference-in-differences (DiD) and double negative controls (DNC), with traditional approaches (ITT, PP, AT, IV). Parameters from influential lumbar discectomy trials informed realistic crossover scenarios with crossover rates ranging from 10% to 40%. Performance was assessed via bias, mean squared error (MSE), and coverage probability (CP) for the method-specific treatment effect estimator across varying sample sizes ( n = 100–500) and confounding structures. ITT, PP, and AT estimators showed severe bias, with CP ≤ 0.1% across the evaluated scenarios. The IV estimator showed small average bias but high estimation variability (MSE = 25.612 at a crossover rate of 40%). The DiD estimator showed low bias and near-nominal coverage under equal confounding (|bias| ≤ 0.003, CP ≥ 94.3%) but showed poor performance when this assumption was violated. In contrast, the DNC estimator remained stable, with |bias| ≤ 0.415, MSE ≤ 2.515, and near-nominal coverage across the evaluated parameter combinations. At n = 100 with a crossover rate of 40%, DNC reduced MSE by > 90% relative to IV while maintaining CP = 97.7%. The DNC approach effectively mitigates unmeasured confounding arising from non-compliance with randomization in surgical RCTs, offering superior accuracy and reliability compared to conventional methods.