Managing Missing Data in the Hospital Survey on Patient Safety Culture
Résumé
Case-wise analysis is advocated for the Hospital Survey on Patient Safety culture (HSOPS).
OBJECTIVES:
Through a computer-intensive simulation study, we aimed to evaluate the accuracy of various imputation methods in managing missing data in the HSOPS.
METHODS:
Using the original data from a cross-sectional survey of 5064 employees at a single university hospital in France, we produced simulation data on two levels. First, we resampled 1000 completed data based on the original 3045 complete responses using a bootstrap procedure. Second, missing values were simulated in these 1000 completed case data for comparison purposes, using eight different missing data scenarios. Third, missing values were imputed using five different imputation methods (1, random imputation; 2, item mean; 3, individual mean; 4, multiple imputation, and 5, sparse nonnegative matrix factorization. The performance for each imputation method was assessed using the root mean square error and dimension score bias.
RESULTS:
The five imputation methods yielded close root mean square errors, with an advantage for the multiple imputation. The bias differences were greater regarding the dimension scores, with a clear advantage for multiple imputation. The worst performance was achieved by the mean imputation methods.
DISCUSSION AND CONCLUSIONS:
We recommend the use of multiple imputation to handle missing data in HSOPS-based surveys, whereas mean imputation methods should be avoided. Overall, these results suggest the possibility of optimizing the HSOPS instrument, which should be reduced without loss of overall information.