Background. Missing data are a significant problem in health-related quality of life (HRQOL) research. We evaluated two imputation approaches: missing data estimation (MDE) and assignment of mean score (AMS). Methods. HRQOL data were collected using the Medical Outcomes Trust SF-12. Missing data were estimated using both approaches, summary statistics were produced for both, and results were compared using intraclass correlations (ICC). Results. Missing data were imputed for 21 participants. Mean values were similar, with ICC > . 9 9 within both the Physical Component Summary and the Mental Component Summary when comparing the two methodologies. When imputed data were added into the full study sample, mean scores were identical regardless of methodology. Conclusion. Results support the use of a practical and simple imputation strategy of replacing missing values with the mean of the sample in cross-sectional studies when less than half of the required items of the SF-12 components are missing. 1. Introduction Health-related quality of life (HRQOL) is an increasingly important outcome in both clinical trials and observational studies [1–4]. It is also a natural choice as an outcome for nursing interventions since interventions are often aimed at improving well-being. This is particularly true in chronic disease management, where a cure does not exist and the goal of treatment is often to optimize comfort, and learn to live with and manage one’s condition [1, 3, 4]. Missing data are a significant problem in HRQOL research due to the potential loss of statistical power as the sample size is reduced and, more importantly, due to the potential for bias [5, 6]. For example, if those who are sicker are less likely to complete the assessment, HRQOL based on those with complete data may be overestimated; conversely, if those who are feeling well drop out of a study, HRQOL may be underestimated [5]. Yet missing data continue to be an issue, even when specific interventions to minimize missing data are used [7]. The potential impact of the problem has been well described [5], including the impact of data that are missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). A number of researcher teams have developed strategies for imputation of missing data, including modified regression estimation [8], missing data estimation (MDE) [9], single and multiple imputation strategies [10], regression-based multipattern imputation [11], last value carried forward/next value carried backward approaches in longitudinal data [12], and hot
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