Analyzing Longitudinal Health-Related Quality of Life Data: Missing Data and Imputation Methods
Health-related quality of life (HRQL) outcomes are frequently incorporated into clinical trials of new medical treatments. Problems associated with missing data, multiplicity of outcomes, and longitudinal data structure complicate the statistical analysis of HRQL data. Various simple and complex imputation techniques and statistical methods have been evaluated to deal with missing at random or missing not at random HRQL data. I used simulations to examine how well four relatively simple imputation methods reproduce known population statistics. The findings suggest that all the imputation methods provide acceptable estimates of the missing HRQL data when less than 10% of the data is missing. The empirical Bayes method was best at reproducing population characteristics, even when missing data rates exceeded 25%. The remaining imputation approaches began to introduce bias into the imputed estimates when missing data rates exceeded 20% across treatment groups. Future research should compare empirical Bayes or multiple imputation with statistical analysis approaches to handling missing HRQL outcome data.
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