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Reducing unmeasured confounding from non-compliance with randomization in surgical RCTs: a simulation study
Zhenzhen Lu1,2, Wenwen Li1,2, Mengyao Wang1,2
1Department of Biostatistics, Zhongshan Hospital, Fudan University, Fenglin Road 180, Xuhui District, Shanghai, 200032, China.
BMC Medical Research Methodology
|June 5, 2026
Summary
Non-compliance in surgical trials causes bias. Double negative controls (DNC) effectively reduce bias and improve accuracy in randomized controlled trials, outperforming traditional methods like intention-to-treat (ITT) and instrumental variable (IV) analysis.
Area of Science:
- Clinical Trials Methodology
- Biostatistics
- Surgical Research
Background:
- Non-compliance with randomization is a significant issue in surgical randomized controlled trials (RCTs).
- This non-compliance introduces unmeasured confounding, which compromises the validity of causal inference.
- Traditional methods like intention-to-treat (ITT), per-protocol (PP), as-treated (AT), and instrumental variable (IV) analysis have limitations in addressing this challenge.
Purpose of the Study:
- To compare the performance of negative control methods (NCM), specifically difference-in-differences (DiD) and double negative controls (DNC), against traditional analytical approaches.
- To evaluate these methods under realistic conditions informed by lumbar discectomy trials, considering varying crossover rates and confounding structures.
Main Methods:
- A simulation study was conducted to assess bias, mean squared error (MSE), and coverage probability (CP) of different estimators.
- Parameters were derived from influential lumbar discectomy trials, simulating crossover rates from 10% to 40%.
- Evaluated methods included ITT, PP, AT, IV, DiD, and DNC across sample sizes of 100 to 500 and diverse confounding structures.
Main Results:
- ITT, PP, and AT estimators exhibited severe bias with minimal coverage probability (CP ≤ 0.1%).
- The IV estimator had low average bias but high variability (MSE = 25.612 at 40% crossover).
- The DNC estimator demonstrated superior stability and accuracy, with low bias (|bias| ≤ 0.415), low MSE (|MSE| ≤ 2.515), and near-nominal coverage across all tested scenarios. DNC significantly outperformed IV, reducing MSE by over 90% at n=100 and 40% crossover while maintaining high CP (97.7%).
Conclusions:
- The double negative control (DNC) approach is highly effective in mitigating unmeasured confounding caused by non-compliance in surgical RCTs.
- DNC offers superior accuracy and reliability compared to conventional methods for causal inference in the presence of non-compliance.
- This method provides a robust solution for improving the validity of findings from surgical randomized controlled trials.
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