• • Aim: To understand the impact on payers, providers, and patients of introducing a new product
  • Cost-effectiveness, cost-utility, cost-benefit, and cost-consequence models, e.g., suitable for reimbursement submissions, or to communicate value in hospital settings
  • Budget-impact and return on investment models
  • Conjoint models to measure sales message impact
  • Assess product profile for key drivers
  • All models are developed de-novo in lie with any relevant local or international guidelines
  • Informs: Messaging and interactions with healthcare providers and payers, patient segmentation, and product development
  • See our work: Impact of product X on procedure Y: model example

What is a budget impact analysis or budget-impact model?

Budget-impact analysis (BIA) or budget-impact model (BIM) is an essential part of any health-economic assessment. In many jurisdictions, BIA is required to gain reimbursement. Even when not mandated, many healthcare providers will not add new products to their formulary without a comprehensive BIA. The purpose of a BIA is to quantify (with an estimate of uncertainty) the financial consequences of adopting a new healthcare product within a specified healthcare setting. Specifically, the BIA predicts how a change in the product used or the product mix in use to treat a condition will impact overall spending on that condition. The BIA considers both the efficacy and safety of the products, and is generally focused on the short- to mid-term horizon of 1 to 5 years. A BIA is often complemented by a cost-effectiveness analysis (CEA). These sometimes cover a longer time horizon, up to patient lifetimes, and can include consideration of patient quality of life. A BIA is used mostly for private payer interactions, whereas a CEA is of more broader interest to physicians, national payers, and patients—and can be published in academic, peer-reviewed journals.

Further Reading:

• Report of the ISPOR Task Force on Modeling Good Research Practices
• Principles of Good Practice for Budget Impact Analysis. Mauskopf et. al, 2007
• Budget Impact Analysis—Principles of Good Practice. Sullivan et. al, 2014

What is a cost-effectiveness analysis or cost-effectiveness model?

The cost of healthcare is always an important aspect, but most decisions on product coverage and reimbursement are not made oblivious to patient outcomes. Clinical data is almost always required, and a key question is the balance between costs and beneficial outcomes. A cost-effectiveness analysis (CEA) or cost-effectiveness model (CEM) estimates the cost of healthcare provision and the potential patient outcomes, both positive and negative (e.g. adverse events), and quantifies the cost-benefit ratio between them. This can be in terms of the cost per health outcome (e.g. hospitalization) avoided or cost per patient life year gained. For healthcare providers, these are commonly presented as an incremental cost-effectiveness ratio (ICER). The ICER is the difference in cost between the two interventions divided by the difference in health outcomes between the two interventions.

Further Reading:

• Good Research Practices for Measuring Drug Costs in CEA. Garrison et. al, 2010.
• CEA Alongside Clinical Trials. Ramsey et. al, 2015.
• Cost-effectiveness versus Cost-Utility Analyses. Jakubiak-Lasocka et. al, 2014.

• Aim: To help you navigate the HTA environment with ease
• NICE, SMC, CADTH, MoIDx, EUnetHTA, FDA
• Cost-effectiveness and cost-utility analysis
• Pricing
• Informs: Regulatory authorities about the clinical, economic, and social value of your product

• Aim: To collect novel data and turn it into evidence -> filling data gaps for your products and generating new messaging potential
• Customized data-collection apps
• Patient data from registries and electronic medical records
• Surveys
• Claims and cost data
• Expert panels and advisory boards
• All data will be analysed to academic standards by experienced data scientists
• Informs: Decisions on market potential, product development, product acquisition, and future studies

Data management tools:

Good data management increases efficiency within the workplace and facilitates the appropriate sharing, reuse, and analysis of data. At the same time, data management tools must focus on data security and limit redundancy of data stored.
Coreva Scientific designs bespoke data management tools that can be used for tracking study development or product trials as part of a value-based healthcare or risk-sharing agreement. These agreements only work correctly if outcomes data can be easily and efficiently collected and analyzed during the pre‑evaluation and evaluation period. For example, our tools are used to collect data on patient outcomes and physician interventions during sedation and provide an instant assessment of the relative risk of events with and without the use of the trialed product. Such tools are designed in collaboration with the primary clinical team trialing the product, ensuring that they are comfortable with the data collection tool before the product evaluation begins.

Physician and payer surveys:

Surveys can be conducted face-to-face, on the telephone, or most-commonly over the internet. Face-to-face and telephone surveys collect a relatively limited volume of data and are best suited to understanding thoughts and feelings that act as decision drivers in the market place. Online surveys can target many more respondents more cheaply and are used to quantify the overall market landscape. Such surveys could be used to understand treatment patterns, the cost of adverse events, or which product feature is perceived as providing the most value. With sufficient data, the statistical analysis and insights provided by a survey specifically designed for your research question can be a very powerful driver of product or messaging enhancement, and thus drive product adoption.

• Aim: To identify, read, and summarize the current literature -> providing you with an overview of the market landscape and any data gaps
• Academic literature databases: PubMed, EMBASE, Cochrane
• Systematic or structured reviews
• Regulatory, guideline, and gray literature reviews
• Full PRISMA compliance
• Informs: Decisions on market potential, product development, product acquisition, and future studies

A literature review summarizes the information on a topic over a specified time period. The report draws common themes together and provides insight into the longitudinal trends where possible. The key to an informative literature review is identifying (all) the relevant literature. This is done via structured searches of bibliographic databases, making the literature identified transparent and reproducible. A structured search generally returns many more ‘hits’ than can be extensively reviewed, and many may not be pertinent to the research question. For this reason, only a limited set of the hits are read in full and summarized. Those selected for full-text review are identified via screening, in which the title and abstract of each hit is compared against pre-defined inclusion and exclusion criteria.

Unlike a research manuscript, literature reviews does not develop new arguments but considers the arguments of all authors/papers included in the review. They can, though, be published if the review leads to novel insights or identifies important data gaps. For publication, literature reviews in the medical field generally need to be systematic. A systematic review is a type of literature review that collects and critically analyzes multiple research studies and is considered to be one of the highest levels of evidence to inform healthcare decision making. Systematic reviews of randomized controlled trials can be found at the Cochrane library. Coreva Scientific performs both structured and systematic literature reviews, these options are compared below:

• Aim: To broadcast new information and value to key audiences -> peer-reviewed publication adds confidence to your product messaging
• Dossiers and reports
• Review articles
• Original research manuscripts
• Congress abstracts, posters and oral presentations
• Videos and animations
• Whitepapers and brochures
• Informs: Stakeholders about current research and high-quality evidence underpinning a product’s value

What is a Scientific Abstract?

The purpose of a scientific abstract is to concisely communicate the question addressed by the research, including the methodology utilized, and the resultant value created and insights gained. The challenge in such a concise document is to make these data unique and interesting enough to be accepted for presentation at the target congress. With extensive experience in academic and commercial science communications and a desire to let the data ‘talk’, Coreva Scientific have had great success in obtaining both oral and poster presentations.

What is a Scientific Manuscript?

Scientific research and review manuscripts aim to engage the reader in an interesting, academic question and communicate the novel findings from the research described. A successful manuscript must pose the question addressed by the research in a manner that draws in the reader, while framing the manuscript narrative to allow a product value story to develop naturally.

As an academic publication, it is crucial that the methodology utilized is described in sufficient detail in the manuscript to allow for replication of the research and full understanding of the results. A manuscript developed by Coreva Scientific will ensure that the results section presents a balanced reflection of the data and outcomes obtained, while maintaining the flow of the value story. Through optimal use of figures and tables, the value of the results can be made more impactful compared with what is possible using text alone. The discussion section is then used to draw the story together, outlining the pro and cons of the analysis and unifying the work with other published analyses.

We cannot promise that manuscripts will be accepted for publication, but with extensive experience in academic and commercial science communications and a desire to let the data ‘talk’, Coreva Scientific and its staff have a substantial body of published manuscripts to their name.

What is a Value Dossier?

A value dossier is a compendium of key evidence supporting a product or procedure. The dossier draws together data from multiple sources to build evidence of value. These data are extensively referenced to published literature and performed clinical trials. A standard dossier contains background information on epidemiology, the therapy area, current clinical practice, disease burden, unmet needs, the product, competitor products, and evidence on clinical, economic, and humanistic value. Many value dossiers include development of product value messages that can be referenced to the supporting evidence. As part of a value dossier project, the development of health economic (budget-impact or cost-effectiveness) models can be included because the data required to inform them is collated as part of the process. Furthermore, the prospect of value creation though meta-analysis can be assessed.
Although generally developed for internal use, it is becoming more common to develop a short-form value dossier alongside the internal evidence compendium. This short-form dossier can be used to present the clinical and economic value of products to healthcare payers and providers.

• Aim: To combine multiple evidence sources into a single estimate of efficacy and/or safety -> quantifying the effectiveness of your product
• Meta-analysis is one of the highest levels of clinical evidence available
• Network meta-analysis to compare multiple products
• Indirect treatment comparison and Bayesian methods
• Informed by systematic review, these studies are publishable in high-aimpact journals and at congresses
• Informs: Value messaging, product positioning, medical communications, and sales strategy

What is Meta-analysis?

In most settings, healthcare providers and payers can select their product of choice from multiple available options. Each of these products is usually backed by positive clinical data, so the question becomes which product is backed by the most evidence and the most robust evidence. This question can be answered via a meta-analysis.

Meta-analysis is viewed as some of the highest levels of clinical evidence available and their outcomes are likely to publishable. It takes data from multiple randomized, controlled trials and synthesises the estimates of effectiveness from each individual trials into an overall estimate of effectiveness. Using meta-analysis, multiple trials showing non-significant or only minor benefits may result in a significant difference between products as the power of the individual studies is magnified. Alternatively, the significant benefit observed in a few trials may be outweighed by multiple trials showing no difference between products.

For further details on meta-analysis please contact us or consult:

• Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. Mother et. al, 2009.

• The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration.Liberati et. al, 2009.

• Systematic review or meta-analysis? Their place in the evidence hierarchy. Paul and Leibovici 2013.
• Systematic review and meta-analysis methodology. Crowther et. al, 2010.

What is Network Meta-Analysis?

Healthcare providers and payers can often select their product of choice from multiple available options. As each of these products is usually backed by positive clinical data, so the question becomes which product is backed by the most evidence and the most robust evidence. This question can be answered via a network meta-analysis.

(Network) Meta-analysis is viewed as some of the highest levels of clinical evidence available. It takes data from multiple randomized, controlled trials and synthesises the estimates of effectiveness from each individual trials into estimates of head-to-head effectiveness. Using network meta-analysis, a probability of superiority per product can be calculated. As with meta-analysis, multiple trials showing non-significant or only minor benefits may result in a significant difference between products as the power of the individual studies is magnified.

In a network meta-analysis, multiple treatments (three or more) are being compared using both direct comparisons and indirect data. Indirect data are linked via a common comparator, e.g. the standard of care or control arm. If an analysis considers the efficacy and safety of two products only connected via indirect data, this is known as an indirect treatment comparison rather than a network meta-analysis. Network meta-analysis and indirect treatment comparison are statistically complex and involve Bayesian analysis. When compared with standard meta-analysis, this can make their outcomes more difficult to communicate effectively. The likelihood of publication, though, is also high and these studies are of interest to physicians as well as healthcare payers and providers.

Further Reading:

• Interpreting Indirect Treatment Comparisons and Network Meta-Analysis for Health-Care Decision Making. Jansen et. al, 2011.
• Conducting Indirect-Treatment-Comparison and Network-Meta-Analysis Studies. Hoaglin et. al , 2011.
• Network meta-analysis. Tianjing Li et. al, 2011.

• Aim: To identify and overcome any barrier that prevents your product from being available to any patient that would benefit from its use
• Landscaping and competitor intelligence
• Budget-impact models
• Risk-sharing agreements
• Value-based healthcare and purchasing
• Pricing and reimbursement
• Informs: Sales strategy, pricing, and reimbursement

What is Risk-sharing?

No matter how beneficial, new products always need to gain sales momentum. This can result in delays in both patients and healthcare systems realizing the positive benefits of adopting new healthcare practices. One reason for this delay, is the uncertainty over potential cost increases or loss of efficiencies. To overcome this, risk sharing agreements have been developed. They are a relatively new and innovative payment or reimbursement model that spreads the risk and opportunity between the two key stakeholders: payers and manufacturers. Under a risk-sharing agreement, the manufacturer and payer agree to link reimbursement to the products effectiveness or benefit. The appeal of such “pay-for-performance” agreements is understandable, as the payer only reimburses the manufacturer for the beneficial health outcomes achieved, rather than for provision of products that may or may not be effective and/or used.

In general, a risk-sharing agreement incorporates a planned assessment period, during which the performance of product is tracked. This includes use of a defines set of patient outcomes and a defined patient population. The level of reimbursement is then linked to the number of positive outcomes achieved. In some cases, it may be that the manufacture offers a tiered rebate on its product if the outcomes pledged are not met.

Clearly, a risk-sharing agreement is about the acceptable splitting of risk. Manufacturers, thus, need to calculate how much they can offer a payer in order to access the payers’ market without taking on too much risk. Coreva Scientific are here to help with the scoping of risk-sharing agreements and to build models to quantify the balance in risk between manufacturers and payers.

Further Reading:
Performance-based risk-sharing arrangements—good practices for design, implementation, and evaluation: ISPOR Good Practices for Performance-based Risk-sharing Arrangements Task Force Report. See also the performance-based risk-sharing arrangements slides by Dr. Lou Garrison.