Online Program

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Thursday, February 14
Thu, Feb 14, 5:30 PM - 7:00 PM
St. James Ballroom
Poster Session 1 and Opening Mixer

Multiple Imputation for Noninferiority Clinical Trials (303907)

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Melanie L. Bell, University of Arizona 
*Brooke A. Rabe, The University of Arizona 

Keywords: non-inferiority, intention-to-treat, missing data, multiple imputation

Non-inferiority (NI) clinical trials aim to show an experimental treatment is therapeutically no worse than the standard of care, particularly if the new treatment is preferred for reasons such as cost, convenience, safety, etc. NI trials are less conservative than superiority or placebo-controlled studies: non-compliance and missing data may increase bias toward the alternative hypothesis. Our objective was to compare multiple imputation (MI) and other methods for analyzing trials with missing data in intention-to-treat (ITT) and per-protocol (PP) populations. We simulated trials with missing data and non-compliance due to treatment inefficacy under varying trial conditions (trajectory of treatment effects, correlation between repeated measures, and missing data type) and assessed these methods by estimating bias, type 1 error and power. We found that a MI model with an auxiliary non-compliance variable performs better than other methods in controlling type I error rates in ITT analyses. A hybrid ITT/PP approach that imputed for non-compliant subjects yielded low type 1 error and was unbiased, offering an alternative estimator under a hypothetical assumption of full compliance.