Clustered and missing data in the US National Trauma Data Bank: implications for analysis. Issue 2 (3rd April 2008)

• Record Type: Journal Article
• Authors: Roudsari, B; Field, C; Caetano, R
• Is Part Of: Injury prevention. Volume 14:Issue 2(2008)
• Page Start: 96
• Page End: 100
• Publication Date: 2008-04-03
• Subjects: Children's accidents -- Prevention -- Periodicals; Accidents -- Prevention -- Periodicals; 617.1
• DOI: 10.1136/ip.2007.017129
• Languages: English
• ISSNs: 1353-8047

Abstract

Abstract : Background: Injury researchers are increasingly using the US National Trauma Data Bank (NTDB). However, there are some methodological issues that might threaten the validity of studies that use this database for injury research. Methods: Two methodological issues were evaluated: clustering of patients within trauma centers and missing data. To illustrate how these issues might affect the results of a study, the following four analytical approaches that evaluated the association between patients' blood alcohol concentration (BAC) in the emergency department (ED), patients' resource utilization, and ED or hospital disposition were compared: (A) deleting subjects with missing BAC and ignoring clustering of patients within trauma centers; (B) deleting subjects with missing BAC while taking into account clustering; (C) using imputed values for patients' BAC and ignoring the clustering issue; (D) using the imputed data while taking into account clustering. Results: Adjustment for clustering of patients within trauma centers increased the CIs in models B and D. The results of the analyses based on imputed data showed that estimates based on complete case analysis were biased. For example, the odds ratio for the use of a head CT scan fell from 1.84 (95% CI 1.49 to 2.28) in approach B to 1.26 (95% CI 0.98 to 1.64) in approach D. Conclusions: Excluding patients with missing values for BAC in studies that evaluate the association between this variable and patients' resource utilization and ED or hospital disposition, using the NTDB, led to biased estimates. Furthermore, ignoring the clustering design led to artificially narrow CIs.