Objective Our objective was to examine perspective and costing approaches used in cost-effectiveness analyses (CEAs) and the distribution of reported incremental cost-effectiveness ratios (ICERs). Methods We analyzed the Tufts Medical Center’s CEA and Global Health CEA registries, containing 6907 cost-per-quality-adjusted-life-year (QALY) and 698 cost-per-disability-adjusted-life-year (DALY) studies published through 2018. We examined how often published CEAs included non-health consequences and their impact on ICERs. We also reviewed 45 country-specific guidelines to examine recommended analytic perspectives. Results Study authors often mis-specified or did not clearly state the perspective used. After re-classification by registry reviewers, a healthcare sector or payer perspective was most prevalent (74%). CEAs rarely included unrelated medical costs and impacts on non-healthcare sectors. The most common non-health consequence included was productivity loss in the cost-per-QALY studies (12%) and patient transportation in the cost-per-DALY studies (21%). Of 19,946 cost-per-QALY ratios, the median ICER was $US26,000/QALY (interquartile range [IQR] 2900–110,000), and 18% were cost saving and QALY increasing. Of 5572 cost-per-DALY ratios, the median ICER was $US430/DALY (IQR 67–3400), and 8% were cost saving and DALY averting. Based on 16 cost-per-QALY studies (2017–2018) reporting 68 ICERs from both the healthcare sector and societal perspectives, the median ICER from a societal perspective ($US22,710/QALY [IQR 11,991–49,603]) was more favorable than from a healthcare sector perspective ($US30,402/QALY [IQR 10,486–77,179]). Most governmental guidelines (67%) recommended either a healthcare sector or a payer perspective. Conclusion Researchers should justify and be transparent about their choice of perspective and costing approaches. The use of the impact inventory and reporting of disaggregate outcomes can reduce inconsistencies and confusion.

Value of information (VOI) analyses can help policy makers make informed decisions about whether to conduct and how to design future studies. Historically a computationally expensive method to compute the expected value of sample information (EVSI) restricted the use of VOI to simple decision models and study designs. Recently, 4 EVSI approximation methods have made such analyses more feasible and accessible. Members of the Collaborative Network for Value of Information (ConVOI) compared the inputs, the analyst's expertise and skills, and the software required for the 4 recently developed EVSI approximation methods. Our report provides practical guidance and recommendations to help inform the choice between the 4 efficient EVSI estimation methods. More specifically, this report provides: (1) a step-by-step guide to the methods' use, (2) the expertise and skills required to implement the methods, and (3) method recommendations based on the features of decision-analytic problems.

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.

Accessed February 19, 2020. https://www.propublica.org/article/medicalprofessors-are-supposed-to-share-their-outside-income-with-the-universityof-california-but-many-dont 6. US Centers for Medicare and Medicaid Services. Open Payment: creating public transparency into industry-physician financial relationships. Published 2019. Accessed February 20 2020. https://www.cms.gov/OpenPayments/ Downloads/forum-op-expansion-support-act-aug2019.pdf

BackgroundFirst-line treatment with nivolumab plus ipilimumab (N+I) or nivolumab plus ipilimumab with two cycles of chemotherapy (N+I+chemotherapy) improve overall survival and progression-free survival for patients with metastatic non-small cell lung cancer (NSCLC), yet researchers have not concomitantly compared the cost-effectiveness of N+I and N+I+chemotherapy with chemotherapy alone.Materials and methodsUsing outcomes data from the CheckMate 227 and CheckMate 9LA phase 3 randomized trials, we developed a Markov model with lifetime horizon to compare the costs and effectiveness of N+I and N+I+chemotherapy versus chemotherapy from the U.S. health care sector perspective. Subgroup analysis by programmed death-ligand 1 (PD-L1) expression levels (≥1% and <1%) and probabilistic analysis were performed.ResultsThe incremental cost-effectiveness ratio (ICER) of N+I versus chemotherapy was $239,072 per QALY, and $838,198 per QALY for N+I+chemotherapy versus N+I. The ICER of N+I versus chemotherapy was $246,584 per QALY for patients with PD-L1 ≥ 1% and $185,620 per QALY for those with PD-L1 < 1%. In probabilistic analysis, N+I had a 2.6% probability of being cost-effective at a willingness-to-pay threshold of $150,000 per QALY. The probability was 0.4% for patients with PD-L1 ≥ 1% and 10.6% for patients with PD-L1 < 1%.ConclusionFirst-line N+I or N+I+chemotherapy for metastatic NSCLC was not cost-effective regardless of PD-L1 expression levels from the U.S. health care sector perspective.

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The expected value of sample information (EVSI) can be used to prioritize avenues for future research and design studies that support medical decision making and offer value for money spent. EVSI is calculated based on 3 key elements. Two of these, a probabilistic model-based economic evaluation and updating model uncertainty based on simulated data, have been frequently discussed in the literature. By contrast, the third element, simulating data from the proposed studies, has received little attention. This tutorial contributes to bridging this gap by providing a step-by-step guide to simulating study data for EVSI calculations. We discuss a general-purpose algorithm for simulating data and demonstrate its use to simulate 3 different outcome types. We then discuss how to induce correlations in the generated data, how to adjust for common issues in study implementation such as missingness and censoring, and how individual patient data from previous studies can be leveraged to undertake EVSI calculations. For all examples, we provide comprehensive code written in the R language and, where possible, Excel spreadsheets in the supplementary materials. This tutorial facilitates practical EVSI calculations and allows EVSI to be used to prioritize research and design studies.

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