Speaker
Description
In many clinical trial analyses, missing data is addressed through multiple imputation (MI) to avoid loss of information and potential bias. However, this approach is not taken into consideration at the planning stage when calculating the sample size. Here, it is common practice to inflate the calculated sample size by an estimated dropout rate in order to maintain the desired power. This results in a discrepancy between the analysis method for which the sample size is calculated and the evaluation method ultimately used.
MI allows uncertainty of the estimator to be properly represented by filling in missing values with several plausible values. Based mainly on the between-imputation (BI) variance, Zha and Harel [1] proposed a power calculation formula, demonstrating that statistical power can be higher when MI is used, which has a particular impact on sample size planning. Further research is needed to systematically evaluate how much power can be gained in order to give recommendations beyond the commonly used inflation of the required sample size.
We extend the simulation study by Zha and Harel [1] with a fixed proportion of missing response under the “missing at random” and “missing completely at random” assumptions, whereby we compare different imputation methods such as predictive mean matching and Bayesian linear regression and vary the number of covariates and their respective relationship to the outcome. We conduct power analyses for different sample sizes. In each scenario, we first validate the provided formula with the simulated power, and then compare the results to the power obtained by complete-case analysis. We propose the number of imputations needed to obtain a robust estimate of the BI variance and thus, the power, in our simulations.
We analyse whether the power gain from multiple imputation in the outcome can be robustly quantified in various settings; particularly in relation to the BI variance. Additionally, we identify scenarios in which MI leads to the most substantial improvements and demonstrate that under optimal conditions, it is possible to eliminate the inflated part of the sample size entirely.
This simulation study aims to contribute to the improvement of sample size calculation for clinical trials that use imputation methods in the primary analysis. Future work expands this framework to include a blinded interim analysis and adaptive sample size adjustment.
[1] Zha, R., Harel, O. Power calculation in multiply imputed data. Stat Papers 62, 533–559 (2021). DOI: 10.1007/s00362-019-01098-8
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