Overview of Services
Real-world evidence
Health economics and outcomes research
Health policy and health systems analysis
Retrospective studies
Prospective observational studies
Epidemiology and biostatistics
Cost-effectiveness models
Budget impact models
Burden of disease models
Literature reviews
Indirect comparisons and meta-analysis
Disease awareness overviews
Mapping of funding and provision of healthcare
Reimbursement applications
Scientific communication
HEOR value strategy advice
Expert panels and advisory boards
Market Access strategies and tools
Launch optimisation
Web-based interactive value communication apps
We understand healthcare
Our way of work
Identify
What is important, and why: Quantify strives to be a partner which helps clients reach their over-arching goals. During setup of a project, investment is made to understand
a) what is important for the client;
b) what is the background and rationale for the project at hand;
c) who are the relevant stakeholders and what their needs are;
d) are there alternative ways of meeting the objective;
and e) what is the most optimal activity to conduct, and how?
By identifying the framework, and defining a common foundation, we are convinced that we optimize the chance for a successful project.
Throughout a standard project, Quantify’s work to Identify includes mapping out the fundamentals, understanding the stakeholder value perception, defining the evidence gaps and identifying how to translate outcome into value.
An important part of evidence generation is identifying critical data gaps and designing a thorough plan for how to best address them. This process generally includes a literature review aiming to map out which data is already available in the published literature, as well as a review of internal data. It may also involve a review of data requirements for health economic modelling and unmet need. In addition to bridging the identified data gaps, evidence generation plans should be cost-effective, consider time constraints and be aligned with both the overall product strategy and the needs of the stakeholders.
When assessing the value of a health care intervention, study endpoints or surrogate markers must often be transformed into measures of concrete value to stakeholders. For example, how do payers view reduced rates of adverse events with a novel treatment; or increasing sleeping time by 20 minutes for patients with insomnia? There are several different methods to translate the benefits into tangible measures; such as Quality Adjusted Life Years or a monetary value based on patient willingness to pay. Depending on identified stakeholder values and data gaps, the optimal data generation activity can be identified.
- Assist in formulating and formalizing research problems and their solutions;
- Analyze different stakeholders’ perceptions of the value of an intervention or reform;
- Organize expert panels and advisory boards to develop and test value messages;
- Identify the data gaps that need to be addressed to substantiate the value messages;
- Provide strategic advice for the evidence generation plan and its components;
- Identify ways to capture the value of clinical improvements from interventions;
- Identify the appropriate data source for conducting a study based on real-world retrospective data;
- Design the optimal combination of cost-effective studies needed to fill the gaps.
Quantify
A wide range of methods and data sources can be employed to quantify the value of health care interventions, understand how health care systems operate, and study treatment patterns, epidemiology or patient segments.
In recent years there has been an increasing interest from regulatory agencies and payers in real-world evidence, reflecting treatment patterns, costs, behavior and effectiveness in clinical practice. In contrast to traditional evidence collected through randomized clinical trials, real-world data provides opportunities to study actual outcomes in real-life setting with longer time horizons, larger patient samples, and without protocol-driven events. They can be used to collect data for input into economic models.
Quantify is at the forefront of retrospective outcomes research with a deep understanding of data access, study design, causal inference, complex analysis, and scientific communication. Retrospective outcomes research can be conducted by extracting information from patient records or by using data available in disease-specific or product-specific databases, epidemiological cohorts, insurance databases and national registers. One advantage of using already collected information from databases and registers is that it offers a resource-efficient way to retrieve large amounts of data. Quantify has the experience and network to access a wide range of data types both in the Nordic region and in other jurisdictions.
In general, access to Swedish data is contingent on approval from the Ethical Review Board following formal ethical vetting, requiring a formal study plan describing the research; and on approval from the respective data holders following their own internal vetting processes. When access is granted to patient-level data for research purposes, data are anonymized before hand-over to the responsible researcher. Study results are communicated on the aggregate level and patient-level data is not shared outside of Quantify. Our research is presented at scientific conferences and published in scientific journals, following peer review.
Data which is readily available in existing databases or registers, may not always be sufficient to fill the identified data gaps. Sometimes, tailor-made primary data collection may need to be carried out and prospectively capture information through paper questionnaires or electronic web-based surveys. The Quantify team has longstanding experience of designing protocols, CRFs, analysis plans as well as executing prospective studies in various countries. Typical cases where primary observational data is of use include:
- Cost-of-illness studies requiring specific components of resource use not captured in typical health-care registers, e.g. informal care, physio therapist visits, travels.
- Quality-of-life studies
- Patient preference and willingness-to-pay studies
- Treatment patterns and epidemiology
CRO Partnership
For prospective multi-country studies we collaborate with our partner, TFS, which has been offering comprehensive clinical development services since 1996. TFS provides a broad set of services, including study and site coordination, patient recruitment, data collection platforms. TFS have presence in most European countries, US, Canada, and Japan.
Economic evaluation plays an instrumental role in medical decision-making and health technology assessment in an increasing number of countries. Reimbursement of health technologies is dependent on the provision of evidence of cost-effectiveness and budgetary impact. Modelling is commonly a useful tool but each problem, disease and purpose needs to be individually considered with respect to model approach and design. Modelling can also be useful for synthesizing data for a particular disease to estimate long-term disease burden, analyze treatment pathways and make projections of the future. The Quantify team has been a part of the development of the health economic modelling field since its inception at the start of the 21st century. The Quantify team have executed more than 200 modelling projects and published over 50 peer-reviewed modelling publications. Key methods include:
- Early modelling
- Within-trial analysis
- Cost-effectiveness modelling
- Budget impact modelling
- Disease burden models
- Treatment pathway models
A critical literature review is one of the most important activities in the process of research and is often included as part of the introduction to a research report or publication. Reviews are not limited to the synthesis of efficacy and safety evidence of medical interventions, but can also address cost-effectiveness evaluations, epidemiology, burden of illness and cost-of-illness, and Patient-Reported Outcomes studies.
Indirect comparisons enable us to combine data from trials which compare different sets of treatments, and form a network of evidence, within a single analysis. This allows us to use all available direct and indirect evidence to inform a given comparison between treatments. A systematic literature review is generally one of the components of an indirect comparison project.
- Assess the most optimal approach and design for collecting novel data to generate relevant evidence
- Plan, design, execute, analyze and report prospective studies in one or several countries
- Execute the entire retrospective study process from end to end including design, ethical considerations, data extraction, data management, statistical analysis and interpretation of results
- Provide strategic and methodological advice and assist with evidence generation plans
- Determine the optimal type of model for any economic evaluation of interest
- Review, improve and adapt existing models to additional markets or new functionalities
- Build the chosen model in the appropriate software, depending on client requirements
- Ad-hoc, structured, and systematic literature reviews
- Design and execute meta-analyses and indirect comparisons
Communicate
The communication of study results needs to be carefully considered to ensure that the key messages are presented in a clear and convincing way. Making the complex simple is critical to successfully conveying study findings to the relevant stakeholders.
- Take an overall responsibility for the successful communication of study results
- Communicate generated evidence in different formats, such as technical reports, global value dossiers, disease area and treatment landscaping reports and local reimbursement dossiers
- Communicate the evidence from studies in peer-reviewed scientific journals
- Present study findings at scientific conferences, to policy makers, as well as to a broader public
- Identify the value strategy and summarize value messages in presentation format, to be used e.g. in sales or market access activities