MD-2: Use valid statistical methods to deal with missing data that properly account for statistical uncertainty due to missingness
Valid statistical methods for handling missing data should be pre-specified in study protocols. The reasons for missing data should be considered in the analysis. A discussion of the potential ramifications of the statistical approach to missing data on the results should be provided. The plausibility of the assumptions associated with the approach should be assessed.
Statistical inference of intervention effects or measures of association should account for statistical uncertainty attributable to missing data. This means that methods used for imputing missing data should produce valid confidence intervals and should permit unbiased inferences based on statistical hypothesis tests. Bayesian methods, multiple imputation, and various likelihood-based methods are valid statistical methods to deal with missing data. Single imputation methods like last observation carried forward, baseline observation carried forward, and mean value imputation are discouraged as the primary approach for handling missing data in the analysis. If investigators do use single-based imputation methods, they must provide a compelling scientific rationale as to why the method is appropriate. This standard applies to all study designs for any type of research question.
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4: Standards for Preventing and Handling Missing Data
MD-2: Use valid statistical methods to deal with missing data that properly account for statistical uncertainty due to missingness
Valid statistical methods for handling missing data should be pre-specified in study protocols. The reasons for missing data should be considered in the analysis. A discussion of the potential ramifications of the statistical approach to missing data on the results should be provided. The plausibility of the assumptions associated with the approach should be assessed. Statistical inference of intervention effects or measures of association should account for statistical uncertainty attributable to missing data. This means that methods used for imputing missing data should produce valid confidence intervals and should permit unbiased inferences based on statistical hypothesis tests. Bayesian methods, multiple imputation, and various likelihood-based methods are valid statistical methods to deal with missing data. Single imputation methods like last observation carried forward, baseline observation carried forward, and mean value imputation are discouraged as the primary approach for handling missing data in the analysis. If investigators do use single-based imputation methods, they must provide a compelling scientific rationale as to why the method is appropriate. This standard applies to all study designs for any type of research question.
Public comments