Decisions on Further Research for Predictive Biomarkers of High-Dose Alkylating Chemotherapy in Triple-Negative Breast Cancer: A Value of Information Analysis

Miquel-Cases, Anna; Retèl, Valesca P.; Harten, Wim H. van; Steuten, Lotte Maria Gertruda

doi:10.1016/j.jval.2016.01.015

Cited by 13 publications

(17 citation statements)

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“…Despite the great potential of VOI analysis to support efficient allocation of resources to research and development, this method is still underused by decision makers, researchers, and funders. 42,52,63,64 Our study provides important insight on further research that can improve disease management in real-world settings and shows that a VOI framework is a valuable tool to inform decision making for precision medicine, providing information on future research and optimal study designs.…”

Section: Resultsmentioning

confidence: 88%

“…Finally, we extrapolated this value to population levels by considering the number of persons likely to benefit from the improved information over its useful lifetime using the disease incidence in the United States, 50,51 a 3% discount rate, 29 and a 10-year decision horizon. 49,52 We considered 3 different study designs to reduce uncertainty in the key parameters. Population-based studies are a preferable approach for collecting additional information on the PREDICT-21-GA joint distribution (assumption 1) and the prevalence of chemotherapy use (assumption 2).…”

Section: Value Of Informationmentioning

confidence: 99%

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

Kunst

Alarid‐Escudero

Paltiel³

et al. 2019

Value in Health

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The 21-gene assay Oncotype DX (21-GA) shows promise as a guide in deciding when to initiate adjuvant chemotherapy in women with hormone receptor-positive early-stage breast cancer. Nevertheless, its routine use remains controversial, owing to insufficient evidence of its clinical utility and cost-effectiveness. Accordingly, we aim to quantify the value of conducting further research to reduce decision uncertainty in the use of the 21-GA. Methods: Using value of information methods, we first generated probability distributions of survival and costs for decision making with and without the 21-GA alongside traditional risk prediction. These served as the input to a comparison of 3 alternative study designs: a retrospective observational study to update risk classification from the 21-GA, a prospective observational study to estimate prevalence of chemotherapy use, and a randomized controlled trial (RCT) of the 21-GA predictive value. Results: We found that current evidence strongly supports the use of the 21-GA in intermediate-and high-risk women. Further research should focus on low-risk women, among whom the cost-effectiveness findings remained equivocal. For this population, we identified a high value of reducing uncertainty in the 21-GA use for all proposed research studies. The RCT had the greatest potential to efficiently reduce the likelihood of choosing a suboptimal strategy, providing a value

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Section: Resultsmentioning

confidence: 88%

Section: Value Of Informationmentioning

confidence: 99%

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

Kunst

Alarid‐Escudero

Paltiel³

et al. 2019

Value in Health

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“…Three additional papers were excluded because they either extended an initial modeling study with long-term data or addressed a specific question about the value of additional research [37–39]. The applications that were evaluated included diagnostic tests for rheumatoid arthritis, a vascular closure device, tissue-engineered cartilage repair, a diagnostic test for stroke recurrence, and genomic profiling in breast cancer (Table 1).…”

Section: Methods For Early Health Technology Assessmentmentioning

confidence: 99%

“…Value Health. 2016 Jun;19(4):419–30.[37]14Kolominsky-Rabas PL, Kriza C, Djanatliev A, Meier F, Uffenorde S, Radeleff J, et al Health Economic Impact of a Pulmonary Artery Pressure Sensor for Heart Failure Telemonitoring: A Dynamic Simulation. Telemedicine and e-Health.…”

Section: Appendixmentioning

confidence: 99%

Emerging Use of Early Health Technology Assessment in Medical Product Development: A Scoping Review of the Literature

et al. 2017

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Early health technology assessment is increasingly being used to support health economic evidence development during early stages of clinical research. Such early models can be used to inform research and development about the design and management of new medical technologies to mitigate the risks, perceived by industry and the public sector, associated with market access and reimbursement. Over the past 25 years it has been suggested that health economic evaluation in the early stages may benefit the development and diffusion of medical products. Early health technology assessment has been suggested in the context of iterative economic evaluation alongside phase I and II clinical research to inform clinical trial design, market access, and pricing. In addition, performing early health technology assessment was also proposed at an even earlier stage for managing technology portfolios. This scoping review suggests a generally accepted definition of early health technology assessment to be “all methods used to inform industry and other stakeholders about the potential value of new medical products in development, including methods to quantify and manage uncertainty”. The present review also aimed to identify recent published empirical studies employing an early-stage assessment of a medical product. With most included studies carried out to support a market launch, the dominant methodology was early health economic modeling. Further methodological development is required, in particular, by combining systems engineering and health economics to manage uncertainty in medical product portfolios.

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“…With the exception of cases in which the health economic model conforms to certain conditions [1,39,7], traditionally, the EVSI has been estimated using computationally intensive nested Monte Carlo simulations [28,2,31,38]. However, recent methodological advances have employed various methods to avoid the need for full nested simulations, irrespective of the underlying model structure [35,27,25,21,24].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Expected Value of Sample Information across Different Sample Sizes Using Moment Matching and Nonlinear Regression

2019

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Background: The Expected Value of Sample Information (EVSI) determines the economic value of any future study with a specific design aimed at reducing uncertainty in a health economic model. This has potential as a tool for trial design; the cost and value of different designs could be compared to find the trial with the greatest net benefit. However, despite recent developments, EVSI analysis can be slow especially when optimising over a large number of different designs. Methods: This paper develops a method to reduce the computation time required to calculate the EVSI across different sample sizes. Our method extends the moment matching approach to EVSI estimation to optimise over different sample sizes for the underlying trial with a similar computational cost to a single EVSI estimate. This extension calculates posterior variances across the alternative sample sizes and then uses Bayesian non-linear regression to calculate the EVSI. Results: A health economic model developed to assess the cost-effectiveness of interventions for chronic pain demonstrates that this EVSI calculation method is fast and accurate for realistic models. This example also highlights how different trial designs can be compared using the EVSI. Conclusion: The proposed estimation method is fast and accurate when calculating the EVSI across different sample sizes. This will allow researchers to realise the potential of using the EVSI to determine an economically optimal trial design for reducing uncertainty in health economic models. Limitations: Our method relies on some additional simulation, which can be expensive in models with very large computational cost. arXiv:1804.09590v1 [stat.ME]

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Decisions on Further Research for Predictive Biomarkers of High-Dose Alkylating Chemotherapy in Triple-Negative Breast Cancer: A Value of Information Analysis

Abstract: Further research on predictive biomarkers for HDAC should focus on gathering data on TPs, prevalence, PPV, TRRs, utilities, and costs from the four ancillary studies to the ongoing RCT.

Cited by 13 publications

References 50 publications

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

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

Emerging Use of Early Health Technology Assessment in Medical Product Development: A Scoping Review of the Literature

Estimating the Expected Value of Sample Information across Different Sample Sizes Using Moment Matching and Nonlinear Regression

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