ArticleMedical decision making : an international journal of the Society for Medical Decision Making2026
A Framework for the Estimation of Quality-Adjusted Life-Years Using Joint Models of Longitudinal and Survival Data.
Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundLength and quality of life are frequently combined in health technology assessments to derive a single, generic measure of health improvement due to a treatment or medicine. One such measure is given by quality-adjusted life-years (QALYs), typically calculated assuming discrete health states and quality-of-life values. The accurate estimation of QALYs is crucial for informed decision making in health care policy and resource allocation; however, traditional methods often rely on assumptions that are sometimes biologically and statistically inappropriate. This study aims to develop a framework for estimating QALYs in populations where survival data are collected that addresses these limitations.
methodsA framework for estimating QALYs in continuous time is introduced, based on joint longitudinal-survival models fitted using maximum likelihood; the proposed framework requires patient-level data, including longitudinal health utility values and overall survival. In contrast to the conventional approach, which involves dichotomising health states and separate models, this method allows the estimation of QALYs from a single model while accounting for all the statistical and biological intricacies of the data, providing a more appropriate estimate for cost-effectiveness modeling. The joint modeling approach is validated using Monte Carlo simulation under realistic data-generating mechanisms.
resultsSimulations showed that the joint longitudinal-survival modeling approach could recover the true QALYs without bias. Conversely, a comparison method based on calculating QALYs directly from the health utility trajectories, ignoring the survival process, was biased under most scenarios.
conclusionsA new methodology for estimating QALYs within a unified, flexible and extensible framework has been developed. This approach can improve QALY estimation and their use in cost-effectiveness analyses in practice, enabling more timely and robust health technology assessments. User-friendly Stata software is provided.
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