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Although this was handled in our statistical analysis, sensitivity analysis using several imputation techniques could provide a better understanding of the variability of the estimates.
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Various missing value imputation techniques have been employed by researchers, such as case-wise deletion, mean value imputation, maximum likelihood, machine learning algorithms including decision tree and MLP.
Missing data will be imputed using multiple imputation techniques.
Missing data will be imputed using multiple imputation techniques [ 62].
Missing values will be imputed using multiple imputation techniques.
For the secondary outcome measures, missing values will be imputed by multiple imputation techniques.
Missing data will be imputed with multiple imputation techniques [ 56, 57].
In the main analysis, missing data will be imputed using multiple imputation techniques [ 80].
If there are many missing data, the missing data will be imputed with multiple imputation techniques.
Over one-third of all cost data were imputed, using multiple imputation techniques.
Missing cost and effect data will be imputed using multiple imputation techniques.
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