Missing data imputation in quality-of-life assessment: imputation for WHOQOL-BREF

Abstract

Introduction: This study investigated the effects of imputing missing data in the WHO Quality of Life Abbreviated Questionnaire (WHOQOL-BREF). The imputation results from both the item and domain levels were compared and the impact of the missing data rate and the number of items included for imputation were examined.

Methods: An empirical analysis and a simulation study were used to examine the effects of missing data rates and the number of items used for imputation on the accuracy for imputation. In the empirical analysis, both item-level and domain-level imputations were performed, and the missing values were imputed using different amounts of data. In the simulation study, sets of 2%, 5% and 10% of the data were drawn randomly and replaced with missing values. Twenty datasets were generated for each situation. The data were imputed and the accuracy of the imputation was reported.

Results: In the empirical study, the number of items used for imputation had only a small impact on the accuracy of imputation. Furthermore, in the simulation study, the accuracy rates of imputation did not significantly change as the proportions of missing data increased. However, the number of items used in the computation did contribute to some extent to the missing values imputed. Extreme responses had the worst computations and the lowest accuracy rates.

Conclusion: It is recommended that as many items as possible be included for imputation within the same domain. However, it is not particularly helpful to use items from different domains for imputation. Researchers should exercise extra caution in interpreting the imputed values of extreme responses.