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

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.

Exploring the misalignment on the value of further research between payers and manufacturers. A case study on a novel total artificial heart

Payers and manufacturers can disagree on the appropriate level of evidence that is required for new medical devices, resulting in high societal costs due to decisions taken with sub-optimal information. A cost-effectiveness model of a hypothetical total artificial heart was built using data from the literature and the (simulated) results of a pivotal study. The expected value of perfect information (EVPI) was calculated from both the payer and manufacturer perspectives, using net monetary benefit and the company's return on investment respectively. A function was also defined, linking effectiveness to market shares. Additional constraints such as a minimum clinical difference or maximum budget impact were introduced into the company's decisions to simulate additional barriers to adoption. The difference in the EVPI between manufacturers and payers varied greatly depending on the underlying decision rules and constraints. The manufacturer's EVPI depends on the probability of being reimbursed, the uncertainty on the (cost-)effectiveness of the technology, as well as other parameters relating to initial investments, operating costs and market dynamics. The use of Value of information for both perspectives can outline potential misalignments and can be particularly useful to inform early dialogs between manufacturers and payers, or negotiations on conditional reimbursement schemes.

HDAC Inhibitors Enhance Efficacy of the Oncolytic Adenoviruses Ad∆∆ and Ad-3∆-A20T in Pancreatic and Triple-Negative Breast Cancer Models

The prognosis for triple-negative breast cancer (TNBC) and pancreatic ductal adenocarcinoma (PDAC) is dismal. TNBC and PDAC are highly aggressive cancers with few treatment options and a potential for rapid resistance to standard-of-care chemotherapeutics. Oncolytic adenoviruses (OAds) represent a promising tumour-selective strategy that can overcome treatment resistance and eliminate cancer cells by lysis and host immune activation. We demonstrate that histone deacetylase inhibitors (HDACi) potently enhanced the cancer-cell killing of our OAds, Ad∆∆ and Ad-3∆-A20T in TNBC and PDAC preclinical models. In the TNBC cell lines MDA-MB-436, SUM159 and CAL51, cell killing, viral uptake and replication were increased when treated with sublethal doses of the Class-I-selective HDACis Scriptaid, Romidepsin and MS-275. The pan-HDACi, TSA efficiently improved OAd efficacy, both in vitro and in SUM159 xenograft models in vivo. Cell killing and Ad∆∆ replication was also significantly increased in five PDAC cell lines when pre-treated with TSA. Efficacy was dependent on treatment time and dose, and on the specific genetic alterations in each cell line. Expression of the cancer specific αvß6-integrin supported higher viral uptake of the integrin-retargeted Ad-3∆-A20T in combination with Scriptaid. In conclusion, we demonstrate that inhibition of specific HDACs is a potential means to enhance OAd activity, supporting clinical translation.

Cost Effectiveness of Teplizumab for Prevention of Type 1 Diabetes Among Different Target Patient Groups

OBJECTIVE

Teplizumab was recently shown to be the first-ever drug to prevent or delay type 1 diabetes mellitus onset in at-risk individuals, especially those with certain genetic and antibody characteristics. However, its potentially high price may pose challenges for coverage and reimbursement for payers and policymakers. Thus, it is critical to investigate the cost effectiveness of this drug for different target individuals.

RESEARCH DESIGN AND METHODS

Using Markov microsimulation modeling, we compared the cost effectiveness of five options for choosing target individuals (i.e., all at-risk individuals, individuals without human leukocyte antigen (HLA)-DR3 or with HLA-DR4 allele, individuals without HLA-DR3 and with HLA-DR4 allele, individuals with anti-zinc transporter 8 (ZnT8) antibody negative, and no provision at all) at different possible prices of teplizumab. Effectiveness was measured by quality-adjusted life-years. Costs were estimated from a health system perspective.

RESULTS

If the price of teplizumab is below US$48,900, treating all at-risk individuals is cost effective. However, it will be cost effective to treat only individuals without HLA-DR3 or with HLA-DR4 alleles for prices between US$48,900 and US$58,200, only individuals both without HLA-DR3 and with HLA-DR4 alleles for prices between US$58,200 and US$88,300, and only individuals with negative ZnT8 antibody status for prices between US$88,300 and US$193,700.

CONCLUSIONS

Cost-effective provision of teplizumab to target individuals depends on the price of teplizumab and genetic and antibody characteristics of treated individuals. As the drug makes its way to the market, findings from this study will help inform policymakers and payers on cost-effective ways to provide this innovative but expensive drug to at-risk individuals.

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

OBJECTIVES

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 between $162 million and $1.1 billion at willingness-to-pay thresholds of $150 000 to $200 000/quality-adjusted life years.

CONCLUSION

Future research to inform 21-GA decision making is of high value. The RCT of the 21-GA predictive value has the greatest potential to efficiently reduce decision uncertainty around 21-GA use in women with low-risk early-stage breast cancer.

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

Background. The expected value of sample information (EVSI) determines the economic value of any future study with a specific design aimed at reducing uncertainty about the parameters underlying 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 optimizing over a large number of different designs. Methods. This article 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 optimize over different sample sizes for the underlying trial while retaining a similar computational cost to a single EVSI estimate. This extension calculates the posterior variance of the net monetary benefit across alternative sample sizes and then uses Bayesian nonlinear regression to estimate the EVSI across these sample sizes. 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 realize the potential of using the EVSI to determine an economically optimal trial design for reducing uncertainty in health economic models. Limitations. Our method involves rerunning the health economic model, which can be more computationally expensive than some recent alternatives, especially in complex models.

Calculating the Expected Value of Sample Information Using Efficient Nested Monte Carlo: A Tutorial

OBJECTIVE

The expected value of sample information (EVSI) quantifies the economic benefit of reducing uncertainty in a health economic model by collecting additional information. This has the potential to improve the allocation of research budgets. Despite this, practical EVSI evaluations are limited partly due to the computational cost of estimating this value using the gold-standard nested simulation methods. Recently, however, Heath et al. developed an estimation procedure that reduces the number of simulations required for this gold-standard calculation. Up to this point, this new method has been presented in purely technical terms.

STUDY DESIGN

This study presents the practical application of this new method to aid its implementation. We use a worked example to illustrate the key steps of the EVSI estimation procedure before discussing its optimal implementation using a practical health economic model.

METHODS

The worked example is based on a three-parameter linear health economic model. The more realistic model evaluates the cost-effectiveness of a new chemotherapy treatment, which aims to reduce the number of side effects experienced by patients. We use a Markov model structure to evaluate the health economic profile of experiencing side effects.

RESULTS

This EVSI estimation method offers accurate estimation within a feasible computation time, seconds compared to days, even for more complex model structures. The EVSI estimation is more accurate if a greater number of nested samples are used, even for a fixed computational cost.

CONCLUSIONS

This new method reduces the computational cost of estimating the EVSI by nested simulation.

Deciphering the roles of lncRNAs in breast development and disease

Breast cancer is the second leading cause of cancer related deaths in women. It is therefore important to understand the mechanisms underlying breast cancer development as well as raises the need for enhanced, non-invasive strategies for novel prognostic and diagnostic methods. The emergence of long non-coding RNAs (lncRNAs) as potential key players in neoplastic disease has received considerable attention over the past few years. This relatively new class of molecular regulators has been shown from ongoing research to act as critical players for key biological processes. Deregulated expression levels of lncRNAs have been observed in a number of cancers including breast cancer. Furthermore, lncRNAs have been linked to breast cancer initiation, progression, metastases and to limit sensitivity to certain targeted therapeutics. In this review we provide an update on the lncRNAs associated with breast cancer and mammary gland development and illustrate the versatility of such lncRNAs in gene control, differentiation and development both in normal physiological conditions and in diseased states. We also highlight the therapeutic and diagnostic potential of lncRNAs in cancer.

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

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.