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Stakeholder involvement has been proposed as a key strategy for appropriately managing value-laden decisions or 'value judgments' in health economics modelling. Philosophers of science, however, conceive of stakeholder involvement in research in conflicting ways, and also propose alternative strategies for 'managing values' in science. Furthermore, all proposed strategies for managing values in science raise philosophical questions and practical challenges that are difficult to resolve. As a result, health economists who seek to appropriately inform value judgments in modelling must currently go without straightforward guidance. There is a need to further explore how health economists should manage value judgments in modelling, taking into account philosophical debates and contextual constraints. This paper discusses core proposals for managing values in science and identifies philosophical questions and practical challenges these proposals leave unresolved. It further considers how this could potentially inform processes to manage value judgments in health economics modelling, using examples from an ongoing modelling project called LEAP (Lifetime Exposures and Asthma Outcomes Projection). We conclude that all strategies to 'manage values' in health economics modelling have strengths and weaknesses, but are generally compatible with one another, suggesting that health economists may use a combination of strategies. Further research is needed to explore the effects of strategies to 'manage values' in health economics modelling. • Many strategies may help manage 'value judgments' in health economics modelling. • We critically discuss proposed strategies using a case study called the LEAP model. • Health economists may combine multiple strategies to 'manage values' in modelling. [ABSTRACT FROM AUTHOR]

Scientific modelling is a value-laden process: the decisions involved can seldom be made using 'scientific' criteria alone, but rather draw on social and ethical values. In this paper, we draw on a body of philosophical literature to analyze a COVID-19 vaccination model, presenting a case study of social and ethical value judgments in health-oriented modelling. This case study urges us to make value judgments in health-oriented models explicit and interpretable by non-experts and to invite public involvement in making them. • Scientific modelling involves making social and ethical value judgments. • Value judgments in modelling include 'representational' decisions and 'inferential' decisions. • Analysis of a COVID-19 vaccination model reinforces the need for public involvement in modelling. [ABSTRACT FROM AUTHOR]

Modelling is a major method of inquiry in health economics. In other modelling-intensive fields, such as climate science, recent scholarship has described how social and ethical values influence model development. However, no similar work has been done in health economics. This study explored the role of social, ethical, and other values in health economics modelling using philosophical theory and qualitative interviews in British Columbia, Canada. Twenty-two professionals working in health economics modelling were interviewed between February and May, 2019. The study findings provide support for four philosophical arguments positing an essential role for social and ethical values throughout scientific inquiry and demonstrate how these arguments apply to health economics modelling. It highlights the role of social values in informing early modelling decisions, shaping model assumptions, making trade-offs between desirable model features, and setting standards of evidence. These results point to several decisions in the modelling process that warrant focus in future health economics research, particularly that which aims to incorporate patient and public values. • Interviews with health economists reinforced philosophical arguments positing a role for values throughout research. • Values informed early modelling decisions, model assumptions, and trade-offs between desirable model features. • Values played a role in assessing consequences of error and taking action under uncertainty in the modelling process. [ABSTRACT FROM AUTHOR]