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Sensitivity analyses in which we tested MMRM models for the effect of treatment on PROs over time (i.e., results presented in Tables 2, 3, 4 and 5) when using datasets produced by multiple imputation also yielded findings (data not shown) much like those observed in the primary analysis.
Moreover, multiple imputation also has assumptions and limitations.
The method of multiple imputation also assumes that data were missing at random.
Multiple imputation also used outcome data, and was implemented using chained equations through the ice command with parameter estimates combined across 20 imputed data sets using mi estimate in Stata.
12 13 That being said, most recommendations now are to use multiple imputation, but subject to some care as it only reduces bias from analysis when data are MAR or MCAR; multiple imputation also requires variables that influence missingness to be included in the imputation model.
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As noted earlier, in addition to nonparametric methods based on variants of hot deck imputation, parametric data fusion in the form of file concatenation and multiple imputation can also be considered.
Multiple imputation is also employed to assess the uncertainty due to imputation since results based on imputation alone, especially when linking imputed locations to other spatial data, may be questionable.
Multiple imputation was also performed in R using the package MICE V2.0: [ 25].
Similar figures were observed for men and women in the validation cohort, where multiple imputation was also used.
Multiple imputation can also be applied in different ways, perhaps most typically to estimate the parameters of a regression model in the context of multivariable analyses.
Instead of the use of inbreeding coefficients, the multiple imputation approach also can be applied to the χ statistics (X i 2 ) of each imputed data set.
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