Real-world Data in Oncology: Introduction
Although randomized trials are the gold standard in research, the need for real-world data is eminent. Real-world data is essential in oncology where discovery and competition are altering at a rapid pace. Since cancer is one of the leading causes of death worldwide, novel therapies and pathways for the introduction of medicines should be assessed across diverse populations and real-life settings. Note that more than 10 million new cancer cases are reported every year throughout the globe (“Cancer Prevention and Control,” 2018). Real-world data can generate insights from routine health care and close the gap between research and routine clinical practice.
To benefit research, drug authorization, and health care, real-world evidence becomes an essential factor in medicine. Since cancer trials can be slow and costly, more and more researchers have started to integrate real-world data in research. Note that real-world data is defined as any information obtained from non-randomized trials and diverse populations. Sources vary from electronic health records to insurance claims (Khozin, Blumenthal & Pazdur, 2017). In fact, the use of electronic health records is increasing. Digital solutions (e.g., wearables and sensors) and social media channels (e.g., Facebook and Twitter) also play a crucial role in oncology. These sources contain vital health-related information, such as demographics, symptoms, long-term effects, adherence rates, and financial burdens. Electronic health advancements benefit interoperability, doctor-patient communication, and compliance. What’s more, real-world data can be employed to reach a balance between external and internal validity and increase patient participation in digital health research. Data can shape the future of oncology care worldwide.
Applications of Real-world Data in Oncology:
Research in the field of oncology is sensitive and challenging. Note that there are various cancer treatments, with radiation therapy and chemotherapy being among the most common interventions (“Types of Cancer Treatments,” 2017). With the increasing use of digital health solutions and experimental cancer therapies, though, real-world data can support numerous aspects of medical research and routine clinical practice. Some of its applications include:
Clinical trials and real-world data
While clinical trials are the gold standard in medical research, real-world data can complement clinical findings. In fact, real-world experiences were the base of medical discoveries for hundreds of years (Corrigan-Curay, Sacks, & Woodcock, 2018). Real-world data can be used to explore biomarkers, validate surrogate endpoints, assess long-term effects, and evaluate treatments in diverse settings. Both prospective (e.g., self-reports) and retrospective (e.g., chart review) data can bring valuable insights into research. In addition, new metrics and technologies (e.g., wearables and apps) can enable data collection from almost every patient with cancer in real time. Such innovations can benefit oncology care and patient-centric methods. Digital solutions can also improve interoperability and research regulations.
In cancer treatments, pragmatic trials reveal some advantages over randomized clinical studies. Note that usually, the rates of participation in clinical trials are low (under 5%) and some groups might be excluded from research (e.g., elderly people) (Khozin, Blumenthal & Pazdur, 2017). Pragmatic trials, on the other hand, combine real-world evidence and information from randomized trials to support the effects of an existing treatment in practice. By including real-world data, community oncologists can get involved (especially in the late stages of the study) helping more patients access novel treatments. Electronic health records become an essential source of information. It’s not surprising that the use of electronic records in health care has increased. Note that digital solutions can facilitate data extraction and data management.
While well-designed observational studies reveal similarities with controlled trials, they generate real-world evidence. Such studies supply health insights without invading patient and clinical behavior. In fact, electronic health records and routine clinical practice observations can provide vital information in cancer research, suggest hypotheses for new clinical trials, and facilitate decision-making (Khozin, Blumenthal & Pazdur, 2017). This approach can benefit subjects with organ dysfunctions and other abnormalities. Note that usually, these patients are excluded from conventional cancer studies. Besides, real-world data can improve the generalizability of any clinical information.
Real-world data can benefit early discovery. Since various factors affect cancer (e.g., genomic characteristics), experts can employ genomic sequencing data to identify biomarkers of response and cohorts, which can improve drug development. Note that advanced data mining techniques can support and improve the validity of biomedical data, as well as benefit target identification (Hughes et al., 2011). To set an example, a pharmaceutical company used a genomic database (with information on tumor sequencing) from subjects with lung cancer and created valid genomic profiles.
Real-world data can support the entire drug development cycle and shorten development times and costs. Note that normally, drug development benefits from aspects, such as accidental discovery, experimentation on fungi and plants, and the profound study of cancer cells and drug targets. Technological solutions and sophisticated simulation techniques can show oncologists how a drug interacts with a target. After that, new drugs are tested on tumor cells and animals, and only after approval – on humans (“Drug Discovery and Development,” 2018). We should mention that when it comes to innovations, biotechnology and pharmaceutical companies are among the leading high-quality providers. Therefore, more and more companies have started to invest in real-world data approaches and electronic sources to improve data collection and analysis – with the sole purpose of benefiting patient outcomes.
Trial design and execution
Real-world sources, such as electronic health records, support the design and execution of clinical studies. Real-world data can improve the trial design, protocol, and feasibility. It can also improve trial execution, particularly by the inclusion of external control arms. Having an external control arm obtained from historical or present populations and real-world settings can improve recruitment and registration. This approach is essential for rare cancers. Note that recently, a drug developer designed a study protocol with high external validity to explore a metastatic cancer population.
Studying the natural history of disease
Designing a study and executing a trial, though, rely on robust findings regarding the natural history of the disease, which encompasses the entire course of the disease (including the pre-symptomatic phase and the point where the patient is cured or not). Real-world data can assess real-life settings (e.g., community-based medical facilities) and examine early screening and assessment methods. The retrospective analysis of electronic health records can also provide valuable insights into risks, early intervention,