کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4302671 | 1288460 | 2011 | 5 صفحه PDF | دانلود رایگان |

BackgroundMissing data has remained a major disparity in trauma outcomes research due to missing race and insurance data. Multiple imputation (M.IMP) has been recommended as a solution to deal with this major drawback.Study DesignUsing the National Data Trauma Bank (NTDB) as an example, a complete dataset was developed by deleting cases with missing data across variables of interest. An incomplete dataset was then created from the complete set using random deletion to simulate the original NTDB, followed by five M.IMP rounds to generate a final imputed dataset. Identical multivariate analyses were performed to investigate the effect of race and insurance on mortality in both datasets.ResultsMissing data proportions for known trauma mortality covariates were as follows: age-4%, gender-0.4%, race-8%, insurance-17%, injury severity score-6%, revised trauma score-20%, and trauma type-3%. The M.IMP dataset results were qualitatively similar to the original dataset.ConclusionM.IMP is a feasible tool in NTDB for handling missing race and insurance data.
Journal: Journal of Surgical Research - Volume 165, Issue 1, January 2011, Pages e37–e41