Estimating mean QALYs in trial‐based cost‐effectiveness analysis: the importance of controlling for baseline utility

TLDR

Baseline mean utility values are often imbalanced between treatment arms, and because they contribute to QALY calculations, such imbalance can bias incremental cost‑effectiveness ratios. The study reviews methods for estimating absolute and differential mean QALYs in randomized trials and demonstrates how baseline utility imbalance affects QALY estimation. The authors compare alternative estimators using data from a recent trial‑based cost‑effectiveness study and a microsimulation, evaluating their performance under baseline imbalance. Widely used estimators produce incorrect differential QALY results when baseline utilities differ, whereas multiple‑regression approaches accurately adjust for baseline differences and provide valid sampling variability estimates. © 2004 John Wiley & Sons, Ltd.

Abstract

Abstract In trial‐based cost‐effectiveness analysis baseline mean utility values are invariably imbalanced between treatment arms. A patient's baseline utility is likely to be highly correlated with their quality‐adjusted life‐years (QALYs) over the follow‐up period, not least because it typically contributes to the QALY calculation. Therefore, imbalance in baseline utility needs to be accounted for in the estimation of mean differential QALYs, and failure to control for this imbalance can result in a misleading incremental cost‐effectiveness ratio. This paper discusses the approaches that have been used in the cost‐effectiveness literature to estimate absolute and differential mean QALYs alongside randomised trials, and illustrates the implications of baseline mean utility imbalance for QALY calculation. Using data from a recently conducted trial‐based cost‐effectiveness study and a micro‐simulation exercise, the relative performance of alternative estimators is compared, showing that widely used methods to calculate differential QALYs provide incorrect results in the presence of baseline mean utility imbalance regardless of whether these differences are formally statistically significant. It is demonstrated that multiple regression methods can be usefully applied to generate appropriate estimates of differential mean QALYs and an associated measure of sampling variability, while controlling for differences in baseline mean utility between treatment arms in the trial. Copyright © 2004 John Wiley & Sons, Ltd.