The public defence will be held as a video conference over Zoom.
The defence will follow regular procedure as far as possible, hence it will be open to the public and the audience can ask ex auditorio questions when invited to do so.
Click here to participate in the public defence
Due to copyright reasons, an electronic copy of the thesis must be ordered from the faculty. In order for the faculty to have time to process the order, it must be received by the faculty no later than 2 days prior to the public defence. Orders received later than 2 days before the defence will not be processed. Inquiries regarding the thesis after the public defence must be addressed to the candidate.
Digital Trial Lecture – time and place
Adjudication committee
- First opponent: Professor Susan Griffin, University of York, UK
- Second opponent: Professor Bjarne Robberstad, University of Bergen
- Third member and chair of the evaluation committee: Associate Professor Jon Michael Gran, University of Oslo
Chair of the Defence
Associate Professor Hans Olav Melberg, University of Oslo
Principal Supervisor
Associate Professor Eline Aas, University of Oslo
Summary
Decision makers frequently face decisions about optimal resource allocation. Given that the available resources are limited, decision makers need to consider the consequences of their decisions on both expected health effects and resource utilization to maximize health outcomes within existing constraints. These decisions are informed by imperfect information, often leading to decision uncertainty and potential negative consequences of wrong decisions. A model-based cost-effectiveness analysis, or other type of economic evaluation, is often used to guide decision makers in their choices by systematically evaluating the magnitude and tradeoffs of the expected health effects and costs of decision options considered.
This PhD dissertation proposes an iterative decision-making framework in health and medicine that formalizes the steps of model-based decision making. Several task forces for decision-analytic modeling and value of information analysis have provided recommendations for different parts of a model-based decision-making analysis. The iterative decision-making framework proposed in this dissertation combines multiple published recommendations to formalize the three key parts involved in undertaking a model-based decision analysis. Furthermore, the proposed framework extends the previous work by propagating the principles of evidence-based medicine and highlighting the importance of the iterative process in decision making. Specifically, the proposed iterative decision-making framework formally combines the conceptualization of the decision problem (Part I), the conceptualization and development of the model (Part II), and the process of model-based decision analysis (Part III). The proposed framework also emphasizes that model-based decision making should not be a one-time exercise but rather a repeated process in light of new, improved evidence. Hence, a value of information analysis, which helps evaluate decision uncertainty, assess the need for further research, and identify the optimal designs of that research, should be perceived as an essential part of the framework for medical decision making, rather than as a subsidiary analysis.
The papers included in this PhD dissertation present applications of the proposed iterative decision-making framework in health and medicine. In Papers I and II, we presented two distinct examples of gathering new evidence to populate the decision-analytic model within the iterative decision-making framework and improve medical decision making. In Paper I, we presented how a large claims dataset can be used to obtain cost and utilization estimates specific to a particular subpopulation. In Paper II, we estimated previously unavailable population-based recurrence rates of colorectal cancer using publicly available registry data in the United States. In this paper, we applied a multistate survival modeling approach to estimate the recurrence rates of colorectal cancer, which are not directly observed in the registry data. In Paper III, we provided a step-by-step guide and practical recommendations for four recently developed approximation methods to estimate expected value of sample information (EVSI). By providing this practical guide and recommendations, we aimed to facilitate the use of EVSI to assess the value of further research before making an immediate decision as part of the iterative decision-making framework. In Paper IV, we illustrated an application of the iterative decision-making framework. In contrast to a number of previous studies addressing a similar decision problem, we found in Paper IV that an immediate decision should not be made for all patients in the population considered.
Additional information
Contact the research support staff.