The validity of using multiple imputation for missing out-of-hospital data in a state trauma registry

Objectives: To assess 1) the agreement of multiply imputed out-of-hospital values previously missing in a state trauma registry compared with known ambulance values and 2) the potential impact of using multiple imputation versus a commonly used method for handling missing data (i.e., complete case analysis) in a typical multivariable injury analysis. Methods: This was a retrospective cohort analysis. Multiply imputed out-of-hospital data from 1998 to 2003 for four variables (intubation attempt, Glasgow Coma Scale score, systolic blood pressure, and respiratory rate) were compared with known values from probabilistically linked ambulance records using measures of agreement (κ, weighted κ, and Bland-Altman plots). Ambulance values were assumed to represent the "true" values for all analyses. A hypothetical multivariable regression model was used to demonstrate the impact (i.e., bias and precision of model results) of handling missing out-of-hospital data with multiple imputation versus complete case analysis. Results: A total of 6,150 matched ambulance and trauma registry records were available for comparison. Multiply imputed values for the four out-of-hospital variables demonstrated fair to good agreement with known ambulance values. When included in typical multivariable analyses, multiple imputation increased precision and reduced bias compared with using complete case analysis for the same data set. Conclusions: Multiply imputed out-of-hospital values for intubation attempt, Glasgow Coma Scale score, systolic blood pressure, and respiratory rate have fair to good agreement with known ambulance values. Multiple imputation also increased precision and reduced bias compared with complete case analysis in a typical multivariable injury model, and it should be considered for studies using out-of-hospital data from a trauma registry, particularly when substantial portions of data are missing.