Mitigating Bias in Health-Related Quality of Life (HRQoL) Estimation Due to Missing Data: A Simulation-Based Study Evaluating Imputation Methods

Abstract

Missing Health-Related Quality of Life (HRQoL) data in clinical studies risk propagating bias into health technology assessments (HTAs) and cost-utility analyses. Despite this, current National Institute for Health and Care Excellence (NICE) guidance offers no specific recommendations for handling missing HRQoL values. Using Monte Carlo simulations (1000 datasets), this study evaluated nine imputation methods, across the missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR) assumptions at levels ranging from 5% to 50%. Performance was assessed using bias, variance, and coverage of the true HRQoL mean. While multiple imputation by chained equations (MICE)-based approaches performed best under MCAR and MAR, all methods showed bias under MNAR, with a delta-pattern mixture model performing the best (relative bias ≤2.1% at all missingness levels but coverage falls to 35.4% at 50% missingness). The choice of imputation method is key to preventing biased results from propagating into cost-effectiveness analysis, which in theory may lead to suboptimal reimbursement decisions and inefficient healthcare spending. To address the lack of explicit guidance from HTA bodies, we have developed a preliminary policy that could be used for HTA submissions: If the missingness pattern is not known and missingness ≤5%, it is suggested that most methods (except GLM) are acceptable (though care should be taken when using CCA and LOCF if MNAR is suspected). It is also suggested that MICE-based techniques are used as the base case for missingness >5%, and delta-PMM used as a sensitivity analysis when data are not MCAR. Further simulation studies would be required to strengthen the suggestions in this preliminary policy; however, the development of universal recommendations would lead to improved consistency and reliability of HTA globally.