Efficient Value of Information Calculation Using a Nonparametric Regression Approach: An Applied Perspective

Value of Information for Clinical Trial Design: The Importance of Considering All Relevant Comparators

Value of Information (VOI) analyses calculate the economic value that could be generated by obtaining further information to reduce uncertainty in a health economic decision model. VOI has been suggested as a tool for research prioritisation and trial design as it can highlight economically valuable avenues for future research. Recent methodological advances have made it increasingly feasible to use VOI in practice for research; however, there are critical differences between the VOI approach and the standard methods used to design research studies such as clinical trials. We aimed to highlight key differences between the research design approach based on VOI and standard clinical trial design methods, in particular the importance of considering the full decision context. We present two hypothetical examples to demonstrate that VOI methods are only accurate when (1) all feasible comparators are included in the decision model when designing research, and (2) all comparators are retained in the decision model once the data have been collected and a final treatment recommendation is made. Omitting comparators from either the design or analysis phase of research when using VOI methods can lead to incorrect trial designs and/or treatment recommendations. Overall, we conclude that incorrectly specifying the health economic model by ignoring potential comparators can lead to misleading VOI results and potentially waste scarce research resources.

The cost-effectiveness of germline BRCA testing-guided olaparib treatment in metastatic castration resistant prostate cancer

BACKGROUND

Olaparib targets the DNA repair pathways and has revolutionized the management of metastatic castration resistant prostate cancer (mCRPC). Treatment with the drug should be guided by genetic testing; however, published economic evaluations did not consider olaparib and genetic testing as codependent technologies. This study aims to assess the cost-effectiveness of BRCA germline testing to inform olaparib treatment in mCRPC.

METHODS

We conducted a cost-utility analysis of germline BRCA testing-guided olaparib treatment compared to standard care without testing from an Australian health payer perspective. The analysis applied a decision tree to indicate the germline testing or no testing strategy. A Markov multi-state transition approach was used for patients within each strategy. The model had a time horizon of 5 years. Costs and outcomes were discounted at an annual rate of 5 percent. Decision uncertainty was characterized using probabilistic and scenario analyses.

RESULTS

Compared to standard care, BRCA testing-guided olaparib treatment was associated with an incremental cost of AU$7,841 and a gain of 0.06 quality-adjusted life-years (QALYs). The incremental cost-effectiveness ratio (ICER) was AU$143,613 per QALY. The probability of BRCA testing-guided treatment being cost effective at a willingness-to-pay threshold of AU$100,000 per QALY was around 2 percent; however, the likelihood for cost-effectiveness increased to 66 percent if the price of olaparib was reduced by 30 percent.

CONCLUSION

This is the first study to evaluate germline genetic testing and olaparib treatment as codependent technologies in mCRPC. Genetic testing-guided olaparib treatment may be cost-effective with significant discounts on olaparib pricing.

Economic value of knowing BRCA status: BRCA testing for prostate cancer prevention and optimal treatment

BACKGROUND

We aimed to estimate the incremental lifetime effects, costs, and net-monetary-benefit (NMB) of knowing BRCA information by testing of patients with low-risk localized prostate cancer (PCa) in the US and guiding subsequent screening and treatment, and the cumulative savings or losses of yearly cohort testing over 16 years. We compared two strategies: (1)'with BRCA information' and (2)'without BRCA information.' We also estimated the expected value of perfect information.

METHODS

The incremental NMB (INMB) quantified the monetized benefit per person of knowing BRCA status. The net-monetized-value of knowing BRCA information was estimated by multiplying the INMB with the eligible population.

RESULTS

The INMBs of knowing BRCA information were $43,357 (payer) and $43,487 (society). in payer and societal perspectives, respectively. Escalated to the eligible patients in 2020, knowing BRCA status resulted in net monetized lifetime value of $1.7 billion (payer and society) for the 2020 cohort; and yielded accumulated net-monetized-value of $28.0 billion (payer) and $28.1 billion (society) over 16 yearly cohorts of eligible PCa patients.

CONCLUSIONS

The economic value of knowing BRCA status for all low-risk localized PCa patients in the US provides short-term and long-term evidence for BRCA testing to screen early and optimize treatment.

An Information Theory Inspired Real-Time Self-Adaptive Scheduling for Production-Logistics Resources: Framework, Principle, and Implementation

The development of industrial-enabling technology, such as the industrial Internet of Things and physical network system, makes it possible to use real-time information in production-logistics scheduling. Real-time information in an intelligent factory is random, such as the arrival of customers' jobs, and fuzzy, such as the processing time of Production-Logistics Resources. Besides, the coordination of production and logistic resources in a flexible workshop is also a hot issue. The availability of this information will enhance the quality of making scheduling decisions. However, when and how to use this information to realize the adaptive collaboration of Production-Logistics Resources are vital issues. Therefore, this paper studies the above problems by establishing a real-time reaction scheduling framework of Production-Logistics Resources dynamic cooperation. Firstly, a real-time task triggering strategy to maximize information utilization is proposed to explore when to use real-time information. Secondly, a collaborative method for Production-Logistics Resources is studied to explore how to use real-time information. Thirdly, a real-time self-adaptive scheduling algorithm based on information entropy is utilized to obtain a stable and feasible solution. Finally, the effectiveness and advancement of the proposed method are verified by a practical case.

Value of Information Analytical Methods: Report 2 of the ISPOR Value of Information Analysis Emerging Good Practices Task Force

The allocation of healthcare resources among competing priorities requires an assessment of the expected costs and health effects of investing resources in the activities and of the opportunity cost of the expenditure. To date, much effort has been devoted to assessing the expected costs and health effects, but there remains an important need to also reflect the consequences of uncertainty in resource allocation decisions and the value of further research to reduce uncertainty. Decision making with uncertainty may turn out to be suboptimal, resulting in health loss. Consequently, there may be value in reducing uncertainty, through the collection of new evidence, to better inform resource decisions. This value can be quantified using value of information (VOI) analysis. This report from the ISPOR VOI Task Force describes methods for computing 4 VOI measures: the expected value of perfect information, expected value of partial perfect information (EVPPI), expected value of sample information (EVSI), and expected net benefit of sampling (ENBS). Several methods exist for computing EVPPI and EVSI, and this report provides guidance on selecting the most appropriate method based on the features of the decision problem. The report provides a number of recommendations for good practice when planning, undertaking, or reviewing VOI analyses. The software needed to compute VOI is discussed, and areas for future research are highlighted.

A Framework to Prioritise Health Research Proposals for Funding: Integrating Value for Money

When making funding decisions, research organisations largely consider the merits (e.g. scientific rigour and feasibility) of submitted research proposals; yet, there is often little or no reference to their value for money. This may be attributed to the challenges of assessing and integrating value of research into existing research prioritisation processes. We propose a framework that considers both the merits of research and its value for money to guide health research funding decisions. A practical framework is developed based on current processes followed by funding organizations for assessing investigator-initiated research proposals, and analytical methods for evaluating the expected value of research. We apply the analytical methods to estimate the expected return on investment of two real-world grant applications. The framework comprises four sequential steps: (1) initial screening of applications for eligibility and completeness; (2) merit assessment of eligible proposals; (3) estimating the expected value of research for the shortlisted proposals that pass the first two steps and ranking of proposals based on return on investment; and (4) selecting research proposals for funding. We demonstrate how the expected value for money can be efficiently estimated using certain information provided in funding applications. The proposed framework integrates value-for-money assessment into the existing research prioritisation processes. Considering value for money to inform research funding decisions is vital to achieve efficient utilisation of research budgets and maximise returns on research investments.

Stochastic impact evaluation of an irrigation development intervention in Northern Ethiopia

Irrigation plays a significant role in achieving food and nutrition security in dry regions. However, detailed ex-ante appraisals of irrigation development investments are required to efficiently allocate resources and optimize returns on investment. Due to the inherent system complexity and uncertain consequences of irrigation development interventions coupled with limited data availability, deterministic cost-benefit analysis can be ineffective in guiding formal decision-making. Stochastic Impact Evaluation (SIE) helps to overcome the challenges of evaluating investments in such contexts. In this paper, we applied SIE to assess the viability of an irrigation dam construction project in northern Ethiopia. We used expert knowledge elicitation to generate a causal model of the planned intervention's impact pathway, including all identified benefits, costs and risks. Estimates of the input variables were collected from ten subject matter experts. We then applied the SIE tools: Monte Carlo simulation, Partial Least Squares regression, and Value of Information analysis to project prospective impacts of the project and identify critical knowledge gaps. Model results indicate that the proposed irrigation dam project is highly likely to increase the overall benefits and improve food and nutrition status of local farmers. However, the overall value of these benefits is unlikely to exceed the sum of the investment costs and negative externalities involved in the intervention. Simulation results suggest that the planned irrigation dam may improve income, as well as food and nutrition security, but would generate negative environmental effects and high investment costs. The Stochastic Impact Evaluation approach proved effective in this study and is likely to have potential for evaluating other agricultural development interventions that face system complexity, data scarcity and uncertainty.

Early cost-effectiveness modeling for better decisions in public research investment of personalized medicine technologies

Millions of dollars are spent on the development of new personalized medicine technologies. While these research costs are often supported by public research funds, many diagnostic tests and biomarkers are not adopted by the healthcare system due to lack of evidence on their cost-effectiveness. We describe a stepwise approach to conducting cost-effectiveness analyses that are performed early in the technology's development process and can help mitigate the potential risks of investment. Decision analytic modeling can identify the key drivers of cost effectiveness and provide minimum criteria that the technology needs to meet for adoption by public and private healthcare systems. A value of information analysis can quantify the added value of conducting more research to provide further evidence for policy decisions. These steps will allow public research funders to make better decisions on their investments to maximize the health benefits and to minimize the number of suboptimal technologies.

Value of Information Choices that Influence Estimates: A Systematic Review of Prevailing Considerations

Background. Although value of information (VOI) analyses are increasingly advocated and used for research prioritization and reimbursement decisions, the interpretation and usefulness of VOI outcomes depend critically on the underlying choices and assumptions used in the analysis. In this article, we present a structured overview of all items reported in literature to potentially influence VOI outcomes. Use of this overview increases awareness and transparency of choices and assumptions underpinning VOI outcomes. Methods. A systematic literature review was performed to identify aspects of VOI analyses that were found to potentially influence VOI outcomes. Identified aspects were grouped to develop a structured overview. Explanations were defined for all items included in the overview. Results. We retrieved 687 unique papers, of which 71 original papers and 8 reviews were included. In the full text of these 79 papers, 16 aspects were found that may influence VOI outcomes. These aspects related to the underlying evidence (bias, synthesis, heterogeneity, correlation), uncertainty (structural, future pricing), model (relevance, approach, population), choices in VOI calculation (estimation technique, implementation level, population size, perspective), and aspects specifically for assessing the value of future study designs (reversal costs, efficient estimator). These aspects were aggregated into 7 items to provide a structured overview. Conclusion. The developed overview should increase awareness of key choices underlying VOI analysis and facilitate structured reporting of such choices and interpretation of the ensuing VOI outcomes by researchers and policy makers. Use of this overview should improve prioritization and reimbursement decisions.

Value of Information Analysis Informing Adoption and Research Decisions in a Portfolio of Health Care Interventions

Background: Value of information (VOI) analysis quantifies the value of additional research in reducing decision uncertainty. It addresses adoption and research decisions simultaneously by comparing the expected benefits and costs of research studies. Nevertheless, the application of this approach in practice remains limited. Objectives: To apply VOI analysis in health care interventions to guide adoption decisions, optimize trial design, and prioritize research. Methods: The analysis was from the perspective of Queensland Health, Australia. It included four interventions: clinically indicated catheter replacement, tissue adhesive for securing catheters, negative pressure wound therapy (NPWT) in caesarean sections, and nutritional support for preventing pressure ulcers. For each intervention, cost-effectiveness analysis was performed, decision uncertainty characterized, and VOI calculated using Monte Carlo simulations. The benefits and costs of additional research were considered together with the costs and consequences of acting now versus waiting for more information. All values are reported in 2014 Australian dollars (AU$). Results: All interventions were cost-effective, but with various levels of decision uncertainty. The current evidence is sufficient to support the adoption of clinically indicated catheter replacement. For the tissue adhesive, an additional study before adoption is worthwhile with a four-arm trial of 220 patients per arm. Additional research on NPWT before adoption is worthwhile with a two-arm trial of 200 patients per arm. Nutritional support should be adopted with a two-arm trial of 1200 patients per arm. Based on the expected net monetary benefits, the studies were ranked as follows: 1) NPWT (AU$1.2 million), 2) tissue adhesive (AU$0.3 milliion), and 3) nutritional support (AU$0.1 million). Conclusions: VOI analysis is a useful and practical approach to inform adoption and research decisions. Efforts should be focused on facilitating its integration into decision making frameworks.