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42 Values for missing data were imputed 10 times using a multiple imputation process with the Mice logarithm (R).
As nearly 20% of household income data were missing, we used a multiple imputation process [ 27] to obtain 4 imputed values, and then created 4 data sets, each of which contained one of the imputed values.
Overall, the median of the multiple imputation process produced an extremely accurate estimation of the actual measured data.
Here again, we observed that the multiple imputation process defined a range in which the actual measurement almost always fell.
Those observations led us to try various parameters that would account for time effect and cell type effect (cross-section Normal vs Cancer) during the multiple imputation process.
These variables were all included within the multiple imputation process.
Similar(50)
The multiple imputation (MI) process has been recommended as the appropriate method to address the uncertainty in the results of economic evaluation due to missing data.
For multiple imputation, this procedure is repeated m times.
We used the multiple imputation method in the process of replacing missing data.
The imputation process creates multiple 'completed' versions of the datasets.
As such, we could use multiple imputation (MI), ignoring the exact process that led to the data being missing.
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