Managing the common problem of missing data in trauma studies

Abstract

Purpose: To provide guidance for managing the problem of missing data in clinical studies of trauma in order to decrease bias and increase the validity of findings for subsequent use.

Organizing construct: A thoughtful approach to missing data is an essential component of analysis to promote the clear interpretation of study findings.

Methods: Integrative review of relevant biostatistics, medical and nursing literature, and case exemplars of missing data analyses using multiple linear regression based upon data from the National Study on the Costs and Outcomes of Trauma (NSCOT) was used as an example.

Findings and conclusions: In studies of traumatically injured people, multiple imputed values are often superior to complete case analyses that might have significant bias. Multiple imputation can improve accuracy of the assessment and might also improve precision of estimates. Sensitivity analyses which implements repeated analyses using various scenarios may also be useful in providing information supportive of further inquiry. This stepwise approach of missing data could also be valid in studies with similar types or patterns of missing data.

Clinical relevance: In interpreting and applying findings of studies with missing data, clinicians need to ensure that researchers have used appropriate methods for handling this issue. If suitable methods were not employed, nurse clinicians need to be aware that the findings may be biased.