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When should I use multiple imputation?
When it is plausible that data are missing at random, but not completely at random, analyses based on complete cases may be biased. Such biases can be overcome using methods such as multiple imputation that allow individuals with incomplete data to be included in analyses.
What do you report multiple imputations to?
Description of Recommended Reporting Practices
- Describe the Nature and Structure of Any Missing Data. Recommendation 1—Report Rates of Missing Data.
- Describe the Imputation Model and Procedures. Recommendation 4—Report Variables Used in the Imputation Phase.
- Describe Any Notable Imputation Results.
When should you impute data?
If there are significant missingness on the baseline variable of a continuous variable, a complete case analysis may provide biased results [4]. Therefore, in all events, a single variable imputation (with or without auxiliary variables included as appropriate) is conducted if only the baseline variable is missing.
When to use multiple imputation for missing data?
MAR allows prediction of the missing values based on the participants with complete data [ 4 ]. If the mechanism depends on the missing data, and this dependency remains even given the observed data, then data are classified as missing not at random (MNAR) [ 4, 5 ].
When to use single variable or monotonic imputation?
Therefore, in all events, a single variable imputation (with or without auxiliary variables included as appropriate) is conducted if only the baseline variable is missing. If both the dependent variable and the baseline variable are missing and the missingness is monotone, a monotonic imputation is done.
Why do we use multiple imputation in randomised trials?
To maintain this baseline comparability of the compared groups, randomised trials are routinely analysed according to the intention-to-treat principle [ 1 ]. However, if some participants are lost to follow-up baseline differences between the compared groups in the analysis may compromise the validity of trial results [ 1 ].
Which is the best form of single imputation?
There are many forms of single imputation, for example, last observation carried forward (a participant’s missing values are replaced by the participant’s last observed value), worst observation carried forward (a participant’s missing values are replaced by the participant’s worst observed value), and simple mean imputation [ 5 ].