Background. Missing data are a significant problem in health-related quality of life (HRQOL) research. We evaluated two\nimputation approaches: missing data estimation (MDE) and assignment of mean score (AMS). Methods. HRQOL data were\ncollected using the Medical Outcomes Trust SF-12. Missing data were estimated using both approaches, summary statistics were\nproduced for both, and results were compared using intraclass correlations (ICC). Results. Missing data were imputed for 21\nparticipants.Mean values were similar, with ICC > .99 within both the Physical Component Summary and theMental Component\nSummary when comparing the two methodologies. When imputed data were added into the full study sample, mean scores were\nidentical regardless of methodology. Conclusion. Results support the use of a practical and simple imputation strategy of replacing\nmissing values with the mean of the sample in cross-sectional studies when less than half of the required items of the SF-12\ncomponents are missing.
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