Computing the Expected Value of Sample Information Efficiently: Practical Guidance and Recommendations for Four Model-Based Methods

Kunst, Natalia; Wilson, Ed; Glynn, David; Alarid‐Escudero, Fernando; Baio, Gianluca; Brennan, Alan; Fairley, Michael; Goldhaber-Fiebert, Jeremy D.; Jackson, Chris; Jalal, Hawre; Menzies, Nicolas A; Strong, Mark; Thom, Howard; Heath, Anna

doi:10.1016/j.jval.2020.02.010

Cited by 59 publications

(53 citation statements)

References 43 publications

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“…First, consideration has to be given to potentially appropriate risk-sharing arrangements. Risk-sharing arrangements can then be assessed quantitatively where possible, for example using the HTA risk analysis framework [17,26], which requires value of information analysis [27][28][29][30]. Further research could inform a more qualitative approach to identifying research targets, study designs and negotiating an acceptable agreement with manufacturers to ensure appropriate pricing.…”

Section: Uncertainty Management In Rare Diseases Going Forwardmentioning

confidence: 99%

Building a trusted framework for uncertainty assessment in rare diseases: suggestions for improvement (Response to “TRUST4RD: tool for reducing uncertainties in the evidence generation for specialised treatments for rare diseases”)

Grimm

Pouwels

Ramaekers

et al. 2021

Orphanet J Rare Dis

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The aim of this letter to the editor is to provide a comprehensive summary of uncertainty assessment in Health Technology Assessment, with a focus on transferability to the setting of rare diseases. The authors of “TRUST4RD: tool for reducing uncertainties in the evidence generation for specialised treatments for rare diseases” presented recommendations for reducing uncertainty in rare diseases. Their article is of great importance but unfortunately suffers from a lack of references to the wider uncertainty in Health Technology Assessment and research prioritisation literature and consequently fails to provide a trusted framework for decision-making in rare diseases. In this letter to the editor we critique the authors’ tool and provide pointers as to how their proposal can be strengthened. We present references to the literature, including our own tool for uncertainty assessment (TRUST; unrelated to the authors’ research), apply TRUST to two assessments of orphan drugs in rare diseases and provide a broader perspective on uncertainty and risk management in rare diseases, including a detailed research agenda.

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Section: Uncertainty Management In Rare Diseases Going Forwardmentioning

confidence: 99%

Building a trusted framework for uncertainty assessment in rare diseases: suggestions for improvement (Response to “TRUST4RD: tool for reducing uncertainties in the evidence generation for specialised treatments for rare diseases”)

Grimm

Pouwels

Ramaekers

et al. 2021

Orphanet J Rare Dis

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“…Specifically, the ''optimal'' estimation method that trades off accuracy, precision, computational time, and ease of implementation will change depending on the health economic model structure, proposed trial design, and analyst expertise. 43 Nonetheless, the analysis in this study has emphasized some distinctions between the methods. First, the RB method is accurate and efficient, provided the analyst can correctly summarize the trial data and fit a regression model, which can be challenging either statistically or computationally.…”

Section: Discussionmentioning

confidence: 98%

“…Specifically, the “optimal” estimation method that trades off accuracy, precision, computational time, and ease of implementation will change depending on the health economic model structure, proposed trial design, and analyst expertise. 43…”

Section: Discussionmentioning

confidence: 99%

Calculating the Expected Value of Sample Information in Practice: Considerations from 3 Case Studies

et al. 2020

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Investing efficiently in future research to improve policy decisions is an important goal. Expected Value of Sample Information (EVSI) can be used to select the specific design and sample size of a proposed study by assessing the benefit of a range of different studies. Estimating EVSI with the standard nested Monte Carlo algorithm has a notoriously high computational burden, especially when using a complex decision model or when optimizing over study sample sizes and designs. Therefore, a number of more efficient EVSI approximation methods have been developed. However, these approximation methods have not been compared and therefore their relative advantages and disadvantages are not clear.A consortium of EVSI researchers, including the developers of several approximation methods, compared four EVSI methods using three previously published health economic models. The examples were chosen to represent a range of real-world contexts, including situations with multiple study outcomes, missing data, and data from an observational rather than a randomized study. The computational speed and accuracy of each method were compared, and the relative advantages and implementation challenges of the methods were highlighted.In each example, the approximation methods took minutes or hours to achieve reasonably accurate EVSI estimates, whereas the traditional Monte Carlo method took weeks. Specific methods are particularly suited to problems where we wish to compare multiple proposed sample sizes, when the proposed sample size is large, or when the health economic model is computationally expensive. All the evaluated methods gave estimates similar to those given by traditional Monte Carlo, suggesting that EVSI can now be efficiently computed with confidence in realistic examples.

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“…• The Professional Society for Health Economics and Outcomes Research (ISPOR) Value of Information Analysis for Research Decisions task force, which aims to develop good practice guidance for using methods of value of information analysis to inform both technology reimbursement decisions and research prioritisation decisions [36] • Collaborative Network for Value of Information (ConVOI) group, an international network working to improve the calculation, adoption and application of value of information methods in clinical and public health research [37,38].…”

Section: Issue Recommendation Actionmentioning

confidence: 99%

How can health economics be used in the design and analysis of adaptive clinical trials? A qualitative analysis

Flight

Julious

Brennan

et al. 2020

Trials

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Introduction: Adaptive designs offer a flexible approach, allowing changes to a trial based on examinations of the data as it progresses. Adaptive clinical trials are becoming a popular choice, as the prudent use of finite research budgets and accurate decision-making are priorities for healthcare providers around the world. The methods of health economics, which aim to maximise the health gained for money spent, could be incorporated into the design and analysis of adaptive clinical trials to make them more efficient. We aimed to understand the perspectives of stakeholders in health technology assessments to inform recommendations for the use of health economics in adaptive clinical trials. Methods: A qualitative study explored the attitudes of key stakeholders-including researchers, decision-makers and members of the public-towards the use of health economics in the design and analysis of adaptive clinical trials. Data were collected using interviews and focus groups (29 participants). A framework analysis was used to identify themes in the transcripts. Results: It was considered that answering the clinical research question should be the priority in a clinical trial, notwithstanding the importance of cost-effectiveness for decision-making. Concerns raised by participants included handling the volatile nature of cost data at interim analyses; implementing this approach in global trials; resourcing adaptive trials which are designed and adapted based on health economic outcomes; and training stakeholders in these methods so that they can be implemented and appropriately interpreted. Conclusion: The use of health economics in the design and analysis of adaptive clinical trials has the potential to increase the efficiency of health technology assessments worldwide. Recommendations are made concerning the development of methods allowing the use of health economics in adaptive clinical trials, and suggestions are given to facilitate their implementation in practice.

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Computing the Expected Value of Sample Information Efficiently: Practical Guidance and Recommendations for Four Model-Based Methods

Cited by 59 publications

References 43 publications

Building a trusted framework for uncertainty assessment in rare diseases: suggestions for improvement (Response to “TRUST4RD: tool for reducing uncertainties in the evidence generation for specialised treatments for rare diseases”)

Building a trusted framework for uncertainty assessment in rare diseases: suggestions for improvement (Response to “TRUST4RD: tool for reducing uncertainties in the evidence generation for specialised treatments for rare diseases”)

Calculating the Expected Value of Sample Information in Practice: Considerations from 3 Case Studies

How can health economics be used in the design and analysis of adaptive clinical trials? A qualitative analysis

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