BACKGROUND: The Focused Assessment with Sonography for Trauma (FAST) examination is an important variable in many retrospective trauma studies. The purpose of this study was to devise an imputation method to overcome missing data for the FAST examination. Owing to variability in patients' injuries and trauma care, these data are unlikely to be missing completely at random, raising concern for validity when analyses exclude patients with missing values. METHODS: Imputation was conducted under a less restrictive, more plausible missing-at-random assumption. Patients with missing FAST examinations had available data on alternate, clinically relevant elements that were strongly associated with FAST results in complete cases, especially when considered jointly. Subjects with missing data (32.7%) were divided into eight mutually exclusive groups based on selected variables that both described the injury and were associated with missing FAST values. Additional variables were selected within each group to classify missing FAST values as positive or negative, and correct FAST examination classification based on these variables was determined for patients with nonmissing FAST values. RESULTS: Severe head/neck injury (odds ratio [OR], 2.04), severe extremity injury (OR, 4.03), severe abdominal injury (OR, 1.94), no injury (OR, 1.94), other abdominal injury (OR, 0.47), other head/neck injury (OR, 0.57), and other extremity injury (OR, 0.45) groups had significant ORs for missing data; the other group's OR was not significant (OR, 0.84). All 407 missing FAST values were imputed, with 109 classified as positive. Correct classification of nonmissing FAST results using the alternate variables was 87.2%. CONCLUSION: Purposeful imputation for missing FAST examinations based on interactions among selected variables assessed by simple stratification may be a useful adjunct to sensitivity analysis in the evaluation of imputation strategies under different missing data mechanisms. This approach has the potential for widespread application in clinical and translational research, and validation is warranted.
BACKGROUND: The Focused Assessment with Sonography for Trauma (FAST) examination is an important variable in many retrospective trauma studies. The purpose of this study was to devise an imputation method to overcome missing data for the FAST examination. Owing to variability in patients' injuries and trauma care, these data are unlikely to be missing completely at random, raising concern for validity when analyses exclude patients with missing values. METHODS: Imputation was conducted under a less restrictive, more plausible missing-at-random assumption. Patients with missing FAST examinations had available data on alternate, clinically relevant elements that were strongly associated with FAST results in complete cases, especially when considered jointly. Subjects with missing data (32.7%) were divided into eight mutually exclusive groups based on selected variables that both described the injury and were associated with missing FAST values. Additional variables were selected within each group to classify missing FAST values as positive or negative, and correct FAST examination classification based on these variables was determined for patients with nonmissing FAST values. RESULTS: Severe head/neck injury (odds ratio [OR], 2.04), severe extremity injury (OR, 4.03), severe abdominal injury (OR, 1.94), no injury (OR, 1.94), other abdominal injury (OR, 0.47), other head/neck injury (OR, 0.57), and other extremity injury (OR, 0.45) groups had significant ORs for missing data; the other group's OR was not significant (OR, 0.84). All 407 missing FAST values were imputed, with 109 classified as positive. Correct classification of nonmissing FAST results using the alternate variables was 87.2%. CONCLUSION: Purposeful imputation for missing FAST examinations based on interactions among selected variables assessed by simple stratification may be a useful adjunct to sensitivity analysis in the evaluation of imputation strategies under different missing data mechanisms. This approach has the potential for widespread application in clinical and translational research, and validation is warranted.
Authors: Jonathan A C Sterne; Ian R White; John B Carlin; Michael Spratt; Patrick Royston; Michael G Kenward; Angela M Wood; James R Carpenter Journal: BMJ Date: 2009-06-29
Authors: John B Holcomb; Deborah J del Junco; Erin E Fox; Charles E Wade; Mitchell J Cohen; Martin A Schreiber; Louis H Alarcon; Yu Bai; Karen J Brasel; Eileen M Bulger; Bryan A Cotton; Nena Matijevic; Peter Muskat; John G Myers; Herb A Phelan; Christopher E White; Jiajie Zhang; Mohammad H Rahbar Journal: JAMA Surg Date: 2013-02 Impact factor: 14.766
Authors: Mackenzie R Cook; John B Holcomb; Mohammad H Rahbar; Erin E Fox; Louis H Alarcon; Eileen M Bulger; Karen J Brasel; Martin A Schreiber Journal: Am J Surg Date: 2015-02-21 Impact factor: 2.565