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Abstract:
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We consider the role of multiple imputation (MI) when analyzing non-inferiority clinical trials with missing data. When the endpoint is measured longitudinally, direct likelihood methods can be used to inform the analysis. We therefore focus on the situation in which the endpoint is not measured longitudinally but other relevant data (patient demographics, early outcomes or biomarkers and surrogate endpoints) are measured at or after baseline prior to planned collection of the primary endpoint data. We present simulation results for various scenarios. As in superiority trials, MI will have most benefit when data are missing at random and the important predictor variables are included in the imputation model. When the endpoint is binary, the ratio of the amount of missing data to the non-inferiority margin will affect the operating characteristics of any analysis strategy (whether imputation based or not), an issue that is unique to non-inferiority trials.
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