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Primary imputation methods (e.g., MMRM, multiple imputation) often rely on MAR. But if data are Missing Not at Random (MNAR), these methods may yield biased results. Sensitivity analyses explore alternative assumptions to assess:
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Preventing missing data during the trial design phase ensures:
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Regulators require clarity on how missing data may have influenced study conclusions. They expect the sponsor to:
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Complete Case Analysis involves analyzing only those subjects for whom all relevant data are available. Any patient with missing data on the outcome or a key covariate is excluded from the analysis.
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Deviations should be categorized and analyzed for their impact. Best practices include:
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Incomplete or vague documentation of missing data raises serious concerns about trial integrity. Accurate records serve multiple purposes:
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Graphical summaries offer intuitive and immediate understanding of where and how data are missing, allowing trial teams to:
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