A Value of Information Analysis of Research on the 21-Gene Assay for Breast Cancer Management

Kunst, Natalia; Alarid‐Escudero, Fernando; Paltiel, A. David; Wang, Shiyi

doi:10.1016/j.jval.2019.05.004

Cited by 18 publications

(10 citation statements)

References 54 publications

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“…We used a combination of regression meta-modeling and Gaussian approximation to calculate EVSI for each proposed study design. These methods have been previously used to conduct this type of analysis (Jutkowitz et al 2017; Kunst et al 2019). Briefly, we applied a parameter-specific variance reduction factor that takes account of the sample size that was used to inform the specific model parameter (i.e., the prior sample size of future studies; Jalal and Alarid-Escudero 2018; Jalal et al 2015).…”

Section: Methodsmentioning

confidence: 99%

Prioritizing Research Informing Antibiotic Prophylaxis Guidelines for Knee Arthroplasty Patients

Kuntz

Alarid‐Escudero

Swiontkowski

et al. 2021

JDR Clinical & Translational Research

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Introduction: Guidelines for routine antibiotic prophylaxis (AP) before dental procedures to prevent periprosthetic joint infection (PJI) have been hampered by the lack of prospective clinical trials. Objectives: To apply value-of-information (VOI) analysis to quantify the value of conducting further clinical research to reduce decision uncertainty regarding the cost-effectiveness of AP strategies for dental patients undergoing total knee arthroplasty (TKA). Methods: An updated decision model and probabilistic sensitivity analysis (PSA) evaluated the cost-effectiveness of AP and decision uncertainty for 3 AP strategies: no AP, 2-y AP, and lifetime AP. VOI analyses estimated the value and cost of conducting a randomized controlled trial (RCT) or observational study. We used a linear regression meta-modeling approach to calculate the population expected value of partial perfect information and a Gaussian approximation to calculate population expected value of sample information, and we subtracted the cost for research to obtain the expected net benefit of sampling (ENBS). We determined the optimal trial sample sizes that maximized ENBS. Results: Using a willingness-to-pay threshold of $100,000 per quality-adjusted life-year, the PSA found that a no-AP strategy had the highest expected net benefit, with a 60% probability of being cost-effective, and 2-y AP had a 37% probability. The optimal sample size for an RCT to determine AP efficacy and dental-related PJI risk would require approximately 421 patients per arm with an estimated cost of $14.7 million. The optimal sample size for an observational study to inform quality-of-life parameters would require 2,211 patients with an estimated cost of $1.2 million. The 2 trial designs had an ENBS of approximately $25 to $26 million. Conclusion: Given the uncertainties associated with AP guidelines for dental patients after TKA, we conclude there is value in conducting further research to inform the risk of PJI, effectiveness of AP, and quality-of-life values. Knowledge Transfer Statement: The results of this value-of-information analysis demonstrate that there is substantial uncertainty around clinical, health status, and economic parameters that may influence the antibiotic prophylaxis guidance for dental patients with total knee arthroplasty. The analysis supports the contention that conducting additional clinical research to reduce decision uncertainty is worth pursuing and will inform the antibiotic prophylaxis debate for clinicians and dental patients with prosthetic joints.

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Section: Methodsmentioning

confidence: 99%

Prioritizing Research Informing Antibiotic Prophylaxis Guidelines for Knee Arthroplasty Patients

Kuntz

Alarid‐Escudero

Swiontkowski

et al. 2021

JDR Clinical & Translational Research

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“…To aid the implementation of the reviewed EVSI methods, we have created a comprehensive GitHub repository that presents the code used to compute EVSI in the original publications, available at https://github.com/convoigroup/EVSIExample. This repository also contains a suite of practical examples that demonstrate EVSI calculations across several real-world applications using common decision-analytic model structures developed in R. 54 Our GitHub repository demonstrates how the reviewed methods can be used in practice and will help analysts to implement VOI methods in their own work and increase the feasibility and accessibility of the EVSI methods as they become an important and required tool.…”

Section: Examples Of the Evsi Methods Applicationmentioning

confidence: 99%

“…This repository also contains a suite of practical examples of EVSI computation that demonstrate EVSI calculations across several real-world examples using common decision-analytic model structures, such as Markov models. We provide both an example that was previously published in the literature [51] and hypothetical examples. These examples are all developed in the statistical computing language R. Thus, our GitHub repository demonstrates how the reviewed methods can be used in practice and will help analysts to implement VOI methods in their own work.…”

Section: Real-world Examplesmentioning

confidence: 99%

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

et al. 2020

Self Cite

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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.

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“…These 2 features, that c c s (n s ) and ENBS s (n s ) are or are well-approximated to be continuously differentiable, are present in EVSI examples in the literature (e.g., see realproblem examples in figure 9 of McKenna et al, 34 figure 5 of Stevenson et al, 35 figure 4 of Cipriano and Weber, 36 and Kunst et al, 37 where variable cost functions are linear and EVSI is a smooth Gaussian approximation). Therefore, features of the necessary optimality conditions for OPT2 provide important insight into many applied research funding allocation problems.…”

Section: Reformulation Of the Optimization Problem To Gain Insight Inmentioning

confidence: 96%

Optimal Allocation of Research Funds under a Budget Constraint

2020

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Purpose. Health economic evaluations that include the expected value of sample information support implementation decisions as well as decisions about further research. However, just as decision makers must consider portfolios of implementation spending, they must also identify the optimal portfolio of research investments. Methods. Under a fixed research budget, a decision maker determines which studies to fund; additional budget allocated to one study to increase the study sample size implies less budget available to collect information to reduce decision uncertainty in other implementation decisions. We employ a budget-constrained portfolio optimization framework in which the decisions are whether to invest in a study and at what sample size. The objective is to maximize the sum of the studies’ population expected net benefit of sampling (ENBS). We show how to determine the optimal research portfolio and study-specific levels of investment. We demonstrate our framework with a stylized example to illustrate solution features and a real-world application using 6 published cost-effectiveness analyses. Results. Among the studies selected for nonzero investment, the optimal sample size occurs at the point at which the marginal population ENBS divided by the marginal cost of additional sampling is the same for all studies. Compared with standard ENBS optimization without a research budget constraint, optimal budget-constrained sample sizes are typically smaller but allow more studies to be funded. Conclusions. The budget constraint for research studies directly implies that the optimal sample size for additional research is not the point at which the ENBS is maximized for individual studies. A portfolio optimization approach can yield higher total ENBS. Ultimately, there is a maximum willingness to pay for incremental information that determines optimal sample sizes.

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A Value of Information Analysis of Research on the 21-Gene Assay for Breast Cancer Management

Cited by 18 publications

References 54 publications

Prioritizing Research Informing Antibiotic Prophylaxis Guidelines for Knee Arthroplasty Patients

Prioritizing Research Informing Antibiotic Prophylaxis Guidelines for Knee Arthroplasty Patients

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

Optimal Allocation of Research Funds under a Budget Constraint

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