Employing real-world evidence allows for the study of many aspects of diseases, such as natural history, patient populations, and outcomes, under everyday conditions. Therefore, real-world evidence has many applications in clinical research, ranging from optimising clinical trial design and population/outcome selection to reducing the burden of regulatory commitments.
There are many advantages to studying a drug’s performance under everyday conditions that cannot be matched in clinical trials, which are essentially controlled experiments. A traditional, Randomised Controlled Trial (RCT), which has a specific design with established inclusion and exclusion criteria, cannot anticipate all the real-world situations that can occur when external factors come into play. Those factors can include behavioural patterns, other therapeutic interventions, and health system effects, such as whether a drug is reimbursed in that country and the treatment protocols in that region or hospital. External factors can be global, local, or specific to the individual patient.
Under clinical trial conditions, a patient with diabetes who is prescribed an anti-diabetic drug must adhere to the study protocol and their outcome is measured based on that protocol. If the patient misses several doses, their data may need to be removed (or censored) from the final analysis dataset. Poor medication adherence is not typically factored into a clinical trial. However, in the real world, people frequently forget a dose of medicine, take the wrong dose, or take it at the wrong time. Medication adherence is just one real-world example; there are many others that need to be considered when trying to truly understand patient outcomes.
For example, inclusion and exclusion criteria for a clinical trial prevent people with specific comorbidities or prior treatments from participating in the trial. Many of these people are likely to receive the intervention in real life despite not qualifying for the RCT. A real-world study generally includes a wider variety of people and circumstances and better reflects everyday situations. However, the experimental design of traditional RCTs allows for easier isolation of the treatment effect of one therapy compared with another as it is less subject to the bias which can present challenges in analysis of Real-world Data (RWD). Thus, RWD are usually employed in conjunction with and in complement to RCTs rather than as a replacement for them.
Despite their obvious advantages, the adoption of real-world studies involves challenges such as gaining access to and managing the heterogeneity and messiness of the data.
The Real-world Evidence (RWE) currently being generated is predominantly from structured data, i.e., electronic medical records, Electronic Health Records (EHRs), and healthcare claims. These data sources, although large and complex, form the tip of the iceberg, as many other data sources, such as free-text entries, paper records, telephonic and video consultations, images, or video camera footage are now available and could form valuable RWE input.
Rare disease research is an area that can benefit greatly from using RWD. Many rare diseases are not well studied, their natural history is unknown, and the outcomes that should be targeted are unclear.
Traditional RCTs include treatment and control arms in which participants receive the current standard of care or placebo. Of course, in the case of rare and orphan disease populations, a sufficiently powered traditional RCT may be very difficult to carry out due to recruitment challenges and the potential absence of a current standard of care. As a result, single-arm trials are increasingly being relied upon as pharmaceutical companies focus on small populations in therapeutic areas of extremely high unmet medical need. This phenomenon is not only limited to rare and orphan disease populations, such as Batten disease and Lennox-Gastaut Syndrome, but also includes smaller underserved segments of more common diseases, such as HER2-negative hormone-positive breast cancer or non-muscle invasive bladder cancer, where there is no current effective treatment available.
Similar scenarios occur with some types of advanced cancers where the disease is both rare and life threatening, making it impractical and unethical to recruit a control population for a clinical trial. In these instances where it is not possible to establish a control group, regulators and payers are increasingly accepting single-arm trials, but they prefer to see some comparative data as part of the marketing authorisation submission. With single-arm trials, RWD can be used to generate an external comparator arm; a practical approach that also saves time and lowers costs.
Regulators are often interested in the use of so-called natural history studies to offer pure external comparators, especially in circumstances where there are no approved treatments or accepted standards of care. However, the term natural history is really a misnomer because all patients receive at least some kind of intervention in the real world. Even in cases where there is no standard of care, doctors always try to alleviate patients’ symptoms with some type of treatment.
Consider Batten disease, which refers to a group of rare, fatal, inherited nervous system disorders that affects about 50 children in the UK. These children have about 20 to 25 seizures a day and reduction in seizure frequency is the desired outcome. Although there is no approved treatment, doctors do prescribe different types of anti-epileptics. Therefore, there cannot be a true natural history study offering a pure non-treated comparison. In such cases, the control group would be an arm that provides information about the treatments used and outcomes for these patients, without the interventional drug in question.
In addition to the use of natural history studies and historical controls, external control arms can leverage data synthesised from other clinical trials that are not part of the same protocol. A synthetic arm is designed by selecting patients from placebo groups in past clinical trials, matching them to participants in the current trial, and then studying the outcomes. Techniques (such as matching techniques) are often used to adjust outcomes for valid comparisons similar to those used in classical realworld studies.
The development of COVID-19 vaccines and drugs provides some good examples of evolving RWD trends because they were created in response to a new disease where there was limited pre-existing clinical data that could be used for RCTs. Much of the COVID-19 disease knowledge and epidemiology was established using RWD and integrated into clinical trials. RWD is also being employed with clinical data to support prescribing decisions for patients with COVID-19.
For example, the World Health Organization (WHO) Solidarity Trial for COVID-19 treatments included RWD in its drug comparisons. It compared four types of existing antiviral or antiinflammatory drugs – remdesivir, hydroxychloroquine, lopinavir/ritonavir, and interferon beta – without a traditional control group. It was not a double-blind study, but a direct comparison study. The results were published by the WHO, and decisions were made by regulatory decision makers and policymakers about which drugs were useful for COVID-19 treatment and which were not recommended. The Solidarity Trial included local hospital standard-of-care procedures, which refer to real-world situations.
In addition to the changes in development and research pathways for COVID-19-related treatments, many non-COVID-19-related clinical trials are currently adversely impacted and progressing slowly because sites are busy caring for COVID-19 patients, physicians are not able to give trials the attention they need, and it is very difficult to recruit patients. As a result, researchers are looking for operational models that are less site dependent and which can leverage RWD. That impetus has further strengthened the trend toward democratising patient data, reinforcing patients’ ownership of their data. As a practical example, by obtaining consent from patients to access their EHRs, insurance claims, and administrative or social demographic data, the burden of collecting such information through the standard trial case report form (a duplicative effort) can be avoided. Blockchain technology is becoming one of the key tools that allows all those data to be brought together in a validated and secure environment, thereby reducing patient recruitment requirements, data collection needs, and dependence on personnel at the sites going forward.
Artificial Intelligence (AI) is also being used for real-world studies. Its earliest and most common uses were in epidemic modelling. AI is now being employed to screen patients and identify those at risk for a particular disease, predict clinical outcomes, and determine optimal drug doses for specific patient groups.
In some Asian countries, pharmaceutical companies are using RWE to obtain marketing approval for traditional herbal medicines without the need for RCTs. As these herbal medicines have already been on market for many years, China’s National Medical Products Administration (NMPA) encourages companies to collect pertinent patient data and submit them for approval under its real-world study regulations.
NMPA released its “Guidelines for Real-World Evidence to Support Drug Development and Review (Interim)” in January 2020 and they were joined by the Center for Drug Evaluation’s “Technical Guidelines for Real-World Research Supporting Child Drug Development and Evaluation (Trial)” in November 2020.
Furthermore, when NMPA approved an Allergan glaucoma treatment product in March 2020, it became the first medical device approved in China using RWE. As the product was already marketed for glaucoma treatment in the US, Allergan could compare RWD from patients in China with the US clinical trial results to determine if there were any ethnic differences between the two patient populations. As no differences were detected, an additional double-blind RCT was not required in China. Using this approach, it took less than one year for Allergan to secure NMPA approval of its glaucoma treatment system.
There are, however, challenges in comparing clinical data between countries. One difficulty is that most clinical trials are conducted from a western market perspective. For example, the comparator chosen in a clinical trial is often from the US, UK, or a European market.
Some diseases are classified differently in the Asian and European markets, presenting significant challenges for clinical trials. For example, certain tumours are defined differently in Asia and Europe depending on the prevalence of tumour sub-types. Another issue is that treatment pathways in some Asian countries may be very different from those in western countries due to drug availability and pricing, physician preferences, and other factors. The contexts can also vary greatly, not just in terms of disease definition, but also social demographics and the availability of traditional medicine methods. As a result, it can be challenging to overlay evidence from one market on the other. RWD can be leveraged to identify and address these differences as part of designing the clinical development program.
While post-marketing studies will likely remain the primary use for RWE for the next 10 years, the COVID pandemic could be a watershed that elicits real change. It has injected a sense of urgency and promoted a shift from the traditional way of doing things to a more innovative and proactive approach using RWE to access more data and accelerate timelines. The drive is toward getting better and broader data and not just clinical trial data.
Regulatory agencies were already becoming more accepting of the use of RWD in clinical trials, but COVID-19 will likely accelerate their adoption. The US Food and Drug Administration (FDA) introduced draft guidelines for “Submitting Documents Using Real- World Data and Real-World Evidence to FDA for Drugs and Biologics” in May 2019 and it plans to issue additional guidance in 2021.
In the interim, the FDA-funded RCT-DUPLICATE project has conducted 10 non-interventional, RWE studies designed to emulate RCTs and evaluate cardiovascular outcomes of anti-diabetic or anti-platelet medications. Initial results from the study, which is being conducted by Brigham and Women's Hospital and Harvard Medical School in close collaboration with the FDA and Aetion, were published in December 2020.
The researchers selected three activecontrolled and seven placebo-controlled RCTs for replication using patient claims data from US commercial and Medicare payers. Nine of the 10 RWE studies achieved at least two of the three agreement metrics. Six of the nine studies also achieved ‘regulatory agreement,’ i.e., interpretation of the results would have resulted in similar regulatory decisions. The results did highlight one significant challenge – as placebos are not administered in everyday clinical practice, they cannot be observed in RWD.
Pharmaceutical companies are also looking to incorporate RWD into decision making earlier in the drug-development process. This change is affecting some pharmaceutical companies’ organisational structures, which, in the past, were very clearly demarcated in terms of pre-launch and post-launch activities. RWD used to be the domain of the postlaunch team and often in the context of market access. Increasingly, the market access staff are joining drug development programs very early, much earlier than they did even as recently as two or three years ago, and bringing their expertise with RWD to address challenges across the development life cycle.
Use of RWD has the potential to help improve development program designs by enabling researchers to test hypotheses and define appropriate clinical trial endpoints for efficacy and safety. The insights gained can help avoid unnecessary clinical trials and improve the probability of success of a development program. RWD can offer regulators and other decision-makers additional insights into the effectiveness of treatments in the ultimate setting in which they will be used by broadening the population for whom the evidence is assembled and by offering insights into how a drug is likely to perform under non-ideal conditions. These data may be less structured and may require more handling and manipulation expertise to make them usable in conjunction with clinical trials, but they can provide a valuable and often more meaningful picture of a product’s potential.