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However, sensitivity analyses accounting for missing data provided no evidence that missing data introduced bias.
Particularly with relatively small amounts of missing data, we observed higher precision, with the drawback that larger amounts of missing data introduced bias.
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Given the frequency with which RBAs are used to assess the effectiveness of college STEM courses, the skew that missing data introduce warrants further investigation.
Marshall et al.[ 26, 27] warned that using a multiple imputation that works well with less than 50% missing data introduces bias in a Cox regression when 75% of the data were missing.
As any missing data introduce uncertainty, we based our analyses on the currently recommended statistical approach [ 29], which is to impute data rather than to trust the complete-case analyses.
We apply the missing data methods introduced in Section 3 on model (22).
As the percent of missing information increased to 30%, or when unbalanced missing data were introduced, MI maintained a high Kappa statistic.
Varying proportions of missing data were introduced into this model and analyses using CC and MI were applied, the latter with varying numbers of auxiliary variables.
Therefore the option to comment on relevance of missing data was introduced and reviewers were able to record "not relevant" if data was missing but deemed non-essential to that particular case, or "relevant" if an item was missing but needed.
Non-random missing data can introduce systematic error and make the study population less representative of the general population.
And finally, missing data may introduce a bias in the relative prevalence of each complication in our population.
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