Estimation of Health-Related Quality of Life in the Presence of Missing Values in EQ-5D

One of the notes worthy problems in analysis of clinical and observational studies is missing data and nonresponse from patients. Turning a blind eye to the missing behavior may provide biased results with overestimated standard errors. The potential impact of the problem may even have more severe impression in estimating health-related quality of life index. This index is an important indicator, widely used in clinical trials for assessing effectiveness of available interventions. Amongst many available measures for estimation of the index, the most rising approach is the EQ-5D preference-based health classifier. This study suggests a cluster-based heuristic algorithm for imputation of missing values in the EQ-5D health classifier to overcome the said problem. The use of auxiliary variable and other dimension’s values as evidences increases the chance of correct identification of the missing value and hence makes it unbiased. Comparisons of bootstrap samples suggest that it overcomes the problem of standard errors and provides efficient estimates.