Abstract
Healthcare policy makers frequently face decisions about how to allocate scarce resources to maximise health. Although these decisions must be made, they are generally based on imperfect information, often resulting in uncertainty. To guide policy makers, the consequences of different decisions are often estimated using a model-based cost-effectiveness analysis (CEA), or other type of economic evaluation. This allows evaluation of the expected health effects and costs of each decision option, as well as the uncertainty surrounding these expected values and the decision. Understanding the consequences of making a suboptimal decision helps inform the potential need for gathering more evidence to support decision making. A value of information (VOI) analysis plays a central role in this assessment.
This chapter introduces the theoretical background for healthcare policy making, and the mathematical and conceptual principles that underpin the methods of VOI analysis. We start with a brief introduction to CEA and the different components and measures that are involved. We thereafter outline the main types of decision-analytic models, and provide a brief overview of the typical kinds of evidence that are used to inform CEAs. Finally, we introduce the idea of uncertainty in model-based CEAs. Specifically, we describe probabilistic analysis: the framework of representing uncertainty by probability distributions for model input parameters. This forms the foundation of VOI analysis. We explain how such probability distributions can be defined based on data, and how probabilistic analysis can be implemented by using Monte Carlo simulation from the joint distribution of model input parameters to propagate uncertainty to model outcomes such as expected costs, health outcomes and net benefits. Finally we describe different measures of decision uncertainty that can be obtained from a probabilistic analysis, which motivates the idea of VOI.
This chapter introduces the theoretical background for healthcare policy making, and the mathematical and conceptual principles that underpin the methods of VOI analysis. We start with a brief introduction to CEA and the different components and measures that are involved. We thereafter outline the main types of decision-analytic models, and provide a brief overview of the typical kinds of evidence that are used to inform CEAs. Finally, we introduce the idea of uncertainty in model-based CEAs. Specifically, we describe probabilistic analysis: the framework of representing uncertainty by probability distributions for model input parameters. This forms the foundation of VOI analysis. We explain how such probability distributions can be defined based on data, and how probabilistic analysis can be implemented by using Monte Carlo simulation from the joint distribution of model input parameters to propagate uncertainty to model outcomes such as expected costs, health outcomes and net benefits. Finally we describe different measures of decision uncertainty that can be obtained from a probabilistic analysis, which motivates the idea of VOI.
| Original language | English |
|---|---|
| Title of host publication | Value of Information for Healthcare Decision-Making |
| Editors | Anna Heath, Natalia Kunst, Christopher Jackson |
| Publisher | Taylor & Francis |
| Chapter | 1 |
| Number of pages | 28 |
| ISBN (Electronic) | 9781003156109 |
| Publication status | Published - 2024 |