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Because of missing data, only 165 physicians were included in the analyses.
Pairwise deletion removes missing data only for variables that are being used in a particular statistical computation.
In this paper, we assume that missing data only occur at testing stage and that training is done with complete data.
Second, for our primary analysis, we have chosen to impute missing data only within studies.
In the case of missing data, only the values available are used.
Imputation of missing data only had a slight impact on these stage distributions.
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Thirty six papers reported at least some information on the amount of missing data, but only seven fully or partially reported comparisons of distributions of key variables in individuals with and without missing data.
These missing data caused only minor problems; only two sequences had missing values for three of the fifty-two parsimoniously variable sites.
However, exclusion of patients with missing data not only wastes valuable data, but can also lead to invalid results if the excluded group is a selected subsample from the source population as indicated in basically all reports in this supplement.
Imputation of missing data is only carried out for individuals having more than 50% non-missing data (at least 3 responses among the 5 items).
We sought to address this in the analysis using multiple imputation methods which assumes missing data are only related to data we have measured and observed.
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CEO of Professional Science Editing for Scientists @ prosciediting.com