🤖 AI Summary
Health economic evaluations often struggle to accurately estimate the joint distribution of potential outcomes under interventions due to the absence of a single comprehensive data source, leading to structural and parametric biases in decision-analytic models. This study proposes a unified framework that integrates decision-analytic modeling within causal inference by leveraging the potential outcomes paradigm to synthesize causal parameters from multiple data sources and approximate intervention effects. It systematically decomposes estimation bias into structural bias and target parameter bias, elucidating how nonstandard parameter specifications induce bias and demonstrating that model credibility critically depends on underlying causal assumptions. By clarifying these mechanisms, the work enhances the transparency and rigor of health policy decision-making.
📝 Abstract
Health economic evaluations are fundamentally concerned with answering causal questions by targeting estimands that contrast the costs and health consequences that would be observed under at least two different interventions. This requires the joint distribution of potential outcomes under each level of intervention, which, with appropriate causal assumptions, can in principle be identified from the joint distribution of observed health outcomes. Such data, however, are rarely available from a single source. This limitation has motivated the use of decision-analytical models to approximate the joint distribution of outcomes under each intervention directly, informed by causal parameters drawn and synthesized from multiple sources, so that the potential outcomes of interest can be approximated as an expectation over the model-implied outcome trajectories. The validity of this approach, however, depends on the credibility of the underlying assumptions. In this work, we formalize this procedure explicitly as a task of causal inference, thereby defining and decomposing decision-analytical model bias into components arising from model structure (model bias) and input parameters (target bias). Because decision-analytical models often rely on unconventional target parameters lacking straightforward observable analogues, and because bias in these parameters can propagate through the model, target bias may arise even in simple settings, a point of central focus in this work. More broadly, this work provides a unifying foundation for medical decision-analytical modelling and causal inference, making explicit the potential for decision-analytical model bias and the role of causal assumptions contributing to it. Ultimately, the resulting clinical decision is only as credible as the assumptions underlying it.