Real World Data and Evidence: Emerging Trends Across Clinical Research

Real-world data (RWD) and real-word evidence (RWE) are increasingly used to support drug development and clinical research across life sciences. RWE studies provide insight into the implementation of therapeutic drugs clinical practice based on RWD of the patient population. RWD and RWE have shown significant value in supporting the regulatory decisions in both the drug development process and healthcare settings.

What are real-world evidence and real-world data?

As defined by the FDA, RWE is “the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD”. RWD can arise from a variety of sources measuring the status of patient health and/or the delivery of therapeutic care. Electronic health records, disease registries and patient-generated data (in home settings) are examples of platforms that record such data. It is important that RWD is captured in a natural, non-interventional manner rather than clinical trial settings. This ensures that the patient/healthcare data captured is representative of real-life circumstances.

Originally, data collected from randomised controlled clinical trials (RCTs) was considered “higher than that in the real world” according to a 2018 review. Randomisation and double-blind protocols in these trials ensure that comparable cohorts are formed. 

Unfortunately, there are some limitations to RCTs. One obvious limitation is the exclusion and inclusion criteria, which exclude a proportion of patients that may be eligible in the real world. This introduces generalisability, and in turn a level of uncertainty about the efficacy of the drug for two reasons:

• As it is only being tested in a specific group of the patient population, the clinical therapeutic response in other patients could vary greatly  

• Factors such as low compliance and reduced tolerability in the clinical setting may not be truly representative of the drugs “performance” in the real-world. 

As a result, RWE has become an important part of evaluating the efficacy of treatments. In the review, it is suggested that data from RCTs could be supplemented with RWD to bridge the gap between the controlled clinical setting of RCTs and the “harsh realities” of the real world.

RWE and RWD: Drug development

RWD and RWE play an important role in FDA regulatory decisions. According to the FDA article, both RWD and RWE support the organisation in monitoring post-market safety and potential adverse effects of therapeutic drugs and devices.This will have a substantial impact on the drug approval process, by understanding the efficacy of treatment in clinical research and real-world patient care. 

In the FDA article, it was also reiterated that “medical product developers are using RWD and RWE to support clinical trial designs (e.g., large simple trials, pragmatic clinical trials) and observational studies to generate innovative, new treatment approaches.” The importance of RWE studies in drug development is emphasised in an article by IQVIA. It is highlighted that drug developers can use RWE in pre-launch studies as insights in selecting clinical trial endpoints and optimising their recruiting strategies.

In clinical pharmacology, RWD has been useful in solving many issues including the optimisation of dose and regimen. The dose and dose regimen at the time of drug approval should be associated with an acceptable benefit-to-risk profile. In some cases, however, pharmacologists question whether these two factors are optimal i.e., could a higher dose potentially improve therapeutic efficacy without a considerable increase in toxicities. This was a particular point emphasised in a 2019 article reviewing the use of RWD and RWE in drug development. In the article, it is suggested that RWD can be used to identify whether modifications in dosing within the real‐world setting are “performed as recommended in the product labeling, potentially providing information on the clinical outcome (both safety and effectiveness) when clinical practice differs.”

RWE and RWD: Clinical research

These pre-launch (RWE) studies are particularly valuable in the advancement of rare disease therapy. A placebo trial arm may be considered unethical and impractical in the small patient populations of rare diseases. In cases like this, RWE studies can “fill the gaps by providing comparator arms and answering preliminary questions about the treatment journey”.

In oncology clinical trials, due to a limited number of patients it is the common side effects of anticancer drugs that are revealed. The more toxic adverse events however, may be missed due to the limited diversity of patients as a result of restrictive inclusion/exclusion criteria. Limited follow-up duration is also suggested to blame for poor estimation of risk associated with adverse events. 

RWD has been suggested to play an important role in addressing these issues, by providing better characterisation of tolerability and adverse events. An accurate toxicity profile for anticancer drugs is critical to inform physicians and patients about the safety of treatment. This was a key point raised in a 2020 review, which suggested that RWD could be a useful “establishing a definitive analysis of benefits and risks associated with treatment for clinical practice guidelines”.

The healthcare community also uses these data to support decision-making and development of guidelines for use of treatment in clinical practice.

Potential challenges

In comparison to clinical trials, RWD is gathered from a multitude of sources, so it is important that the relevant information is captured. Data access and quality are two of the challenges when using RWD, as highlighted in a 2019 article. The article raises the fact that RWD can come from many types of databases, including pharmacy dispensing data or electronic health records (EHR). The data often varies in quality and can include missing data. Pharmacy dispensing records, for example, are not always the most reliable. Even if a prescription is filled for a patient, there is no data to confirm they are taking as prescribed or even taking a drug at all. Over-the-counter drugs are often not documented in RWD. This introduces a potential confounding factor that can comprise the reliability of RWD.

Integrating data from the numerous RWD sources is also a challenge. Combining data from different platforms including EHRs, digital health devices and genomic imaging is a difficult task. As suggested in the aforementioned article, technological solutions like Natural Language Processing could integrate the data to create a more comprehensive picture and novel insights. 

Charlotte Di Salvo, Lead Medical Writer
PharmaFeatures