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CD4 cell count was missing in 29 of 204 cases with HIV co-infection; for multivariate analyses involving CD4 count, missing values were imputed using a multiple imputation strategy [ 12].
Additionally, when we apply the FE-estimator, we find a parameter estimate of 0.376 based on the multiple imputation strategy, which resembles the results from the main sample but is not significant at conventional levels (α = 0.152).
A multiple imputation strategy will be used in case of missing explanatory variables.
The multiple imputation strategy developed was then validated on the 30% validation sample.
This multiple imputation strategy is implemented using MI, MIANALYZE, GENMOD, and NLMIXED procedures in SAS.
The authors wish to thank Michela Baccini for her useful suggestions in specifying the multiple imputation strategy.
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To the best of our knowledge, limited work has been done on comparing different multiple imputation strategies for missing binary outcomes in CRTs.
These amounts of missingness were chosen to cover the range of possible missingness in practice [ 30]. 2) Applied the above multiple imputation strategies to generate m = 5 datasets.
Since there are no missing data in the CHAT dataset, it provides us a convenient platform to design a simulation study to compare the imputed and the observed values and further investigate the performance of the different multiple imputation strategies under different missing data mechanisms and percentages of missingness.
It is important to mention that, although our multiple imputations strategy resulted in a winning contribution, it does not provide any insights into the biomolecular system underlying the data.
Standard multiple imputation (MI) strategies may not be appropriate to impute missing data from CRTs since they assume independent data.
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