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Blog
Real World Evidence Studies: Getting started
Jul 30, 2020
Real world evidence (RWE) has become a powerful tool in the life sciences industry to help developers demonstrate the safety and efficacy of their treatments to regulators, payers, providers, and patients.
But many developers are still uncertain about the best approach to using real world data (RWD) and RWE to maximize their clinical and commercial results.
To provide greater clarity into this important data trend, experts from across IQVIA produced a series of webinars and blogs exploring the evolution of RWE methodologies, and how to use these methods to inform decision-making across the drug development lifecycle.
The basics of RWE
RWD and RWE refer to any relevant patient-level data not collected through a typical, most often randomized, clinical trial (RCT).
Clinical trials are usually designed to prove whether a medicine or treatment works under specific conditions and as such have a large number of inclusion and exclusion criteria. This limits the number of patients eligible for the clinical trial and also limits the generalizability of the trial’s results to the other patients with the disease of interest. RWE adds perspective to RCT results by offering a granular view of the patient journey and treatment experience in the real world. These observations can be gathered from a variety of non-interventional sources, including data from routine hospital or doctor visits, patient registries, claims reports, connected devices, prescription data, observational studies, and other primary and secondary patient-level data.
The diverse patient population, as well as scope and scale of RWD sources makes it easier to generalize long-term outcomes and risks of a treatment compared to RCT results. Because RWE studies have few exclusion criteria, they represent a more realistic population of users. The varying ages, comorbidities, use of additional medications, and healthcare behaviors of these populations provide new insights into a treatment’s safety and effectiveness, and make it possible to identify trends in specific sub-populations. Real world studies can also be conducted for much longer time frame, often at a fraction of the cost of clinical studies.
Using real world data and evidence helps bridge the evidence gap from the RCT to understand the true efficacy and safety profile of the product in the real world.
However, there are methodological aspects that need to be considered. Because RWD is collected in non-controlled settings, the overall quality and completeness of the data will vary; a variety of factors may drive who gets access to which treatment, how it is used, and the underlying risk of clinical outcomes. The data itself may be inconsistently collected, duplicated, or missing some areas of information. It’s often provided by the patients themselves who can provide important information about how an illness affects them but are unlikely to use medical terms to describe what they are experiencing. This means companies must have a clear RWE strategy with appropriate analysis techniques that will meet the requirements of regulators, payers, providers, and patients.
When to use RWE
The use of RWE has become more common in post-launch applications. However, RW studies can add value throughout the drug development and commercialization lifecycle:
Pre-launch: RWE studies can be used early in the drug development process to identify disease burden on both patients and the healthcare system, treatment patterns, and disease epidemiology. Developers can use these insights to select clinical trial endpoints and target recruiting efforts; and later on, they can be used to model cost-effectiveness to health technology assessment (HTA) groups and payers, to show physicians how the product will fit in the current clinical practice, and to help identify undiagnosed patients.
These pre-launch studies are especially valuable in the development of treatments for rare diseases and cancers that tackle unmet medical needs in small populations, including situations where placebo trial arms may be unethical or impractical. In these cases, RWE studies can fill the gaps by providing comparator arms and answering preliminary questions about the treatment journey.
Post-launch: At this stage, RWE studies can be used to understand real world treatment patterns and risks of outcomes (as they will typically vary from what is seen in clinical trials), and gain conditional approval from regulators, demonstrate real world benefits for HTAs and payers, and gain leverage in pricing negotiations. These data can also be used with physicians and patients to demonstrate treatment benefits, and to help shape value messaging.
Line extensions: RWE studies that demonstrate effectiveness and acceptability in new patient groups can help make the case for line extensions to regulators, support the case to payers for broader access, and reassure physicians that it is appropriate for new populations.
Criteria for RWE studies
Regulators, payers and providers are increasingly seeking RWE to demonstrate safety, efficacy, and value over current standards of care. However, developers have to be confident that the RWE studies they conduct will adequately support their claims to these stakeholders.
To do that, the studies should meet the following criteria:
- Relevance: Data should be selected based on their ability to address specific regulatory questions, and provide sufficient details on exposure, covariates, and outcomes for the study purpose. RWD should be representative of patients with the target condition and have sufficient size and follow-up to demonstrate benefit.
- Reliability: Consider whether the source of data identifies patients of interest, outcomes of interest, and covarients. Has it been used for RWE before? Are there enough patients of interest? How complete are the data? Is there missing data?
- Reporting: RWD sources should follow reporting standards and document data elements and definitions, data aggregation methodology, and data collection time windows.
- Transparency: The source verification, data transformation and auditing procedures for completeness and consistency must be transparent to be acceptable.
- Common data model: When using multiples databases, it is often beneficial to leverage a common data model that features common terminologies, vocabularies, and coding schemes across multiple sources.
- Gaps: A variety of RWD sources is often needed to fill gaps in the data.
In many cases, regulators will consider RWE to support accelerated reviews and early authorization if a treatment addresses an unmet medical need in a serious condition. In these cases, the data must prove that an unmet medical need will be fulfilled, and the benefits of the medicinal product’s immediate availability to public health will outweigh risks related to the need for further data.
For companies interested in creating or honing their RWE strategy, IQVIA’s experts advise them to start planning their RWE efforts early in the development process, and take a long view of how these studies can add value across the product portfolio. When the RWE strategy aligns with specific stakeholder needs, and captures the right data to answer compelling questions, it can add powerful value that will drive commercial success.
IQVIA can help you analyze and understand the ever-expanding ecosystem of clinically-rich data in the context of your organization’s needs. By combining advanced analytics with unparalleled data and scientific expertise, IQVIA provides customers with innovative real world evidence approaches to meet stakeholder needs throughout the product lifecycle.
To learn more, click here to listen to the IQVIA webinar, The Principles of Real World Evidence, or contact us.
eBook: Explore Transformative RWD Sources
Discover how you can leverage RWE to accelerate study timelines, shorten time to market, cut research costs, and more. In this interactive eBook, read case examples on how key client questions were answered in various therapeutic areas and glean insights on how you can use RWD to answer challenging research questions.