With the rise in digital technologies, there has been an explosion in the volume and type of data sources. We can obtain information about individual health from social media data and mobile apps, to wearable sensors and electronic health records. Corporations and governments even use insurance claims data as sources of data for analyses.
This data could yield a more robust and complete picture of diseases, the patient journey, and the effectiveness of interventions in the real world. This in turn is often used by life sciences leaders to make better drug development, reimbursement, and clinical decisions. However, apart from accessing and curating this data, we also need to harness advanced analytical techniques to generate evidence, including the sophisticated use of statistical methods. The real-world evidence (RWE) data sciences team therefore must be chosen carefully to take on the challenges of these novel uses of data.
The delivery of RWE-analyses requires more than simply statistical knowledge. The variety of RWE methodologies reflect the range of opportunities sponsors have, to cast their assets in the best light. In this blog we outline the RWE design and staffing needs of a specific kind of observational study, namely natural history studies, as regulators are increasing demand for these explanatory assessments of the biochemistry of disease progression.
An increasing number of trial sponsors are confronted with Regulators requesting that they conduct comprehensive natural history studies before beginning a clinical research study. The purpose of these studies is to have a clear account of the natural progression of disease in a specific target population, thereby clarifying what the causal mechanisms of an intervention must contend with when displaying proof of concept or even efficacy and effectiveness. Most natural history studies are therefore conducted prior to Phase 1 of a trial, although in numerous cases when such studies have not been conducted, Regulators request them later during a trial program.
Support Trial Design with RWD
While natural history studies are crucial in understanding the biochemistry of a disease, few sponsors are willing to expend limited resources when no new therapy is being tested. There are often challenges to recruitment, such as too few patients in a therapeutic area to use for the study of a disease’s natural progression; and even when there are numerous patients available, they might be more inclined to enroll in studies where they have at least a chance of being enrolled into an active arm of a trial. Therefore, the use of existing data to conduct a natural history study has become a key factor in strategic study design.
A common way to recycle existing data for a natural history study is to purchase clinical or observational data, which makes optimizing upon already collected data the strategic goal. Sponsors can appeal to electronic health records, or make more use of claims data, to get a sense of natural disease progression. Regulators might be wary of statisticians appearing to manipulate data, to make it appear as if evidence is saying something other than what it should be saying. Rather, an experienced statistical expert can highlight the data in its best light, to provide sound results from data already collected. This can also serve to shorten timelines, as much of the work comes from the analyses rather than through enrollment and collection.
Need for RWE Expertise
In situations where there is insufficient data or there is lack of homogeneity between historical data and that needed for a trial, working with a joint team of statisticians and data managers can help identify opportunities to recycle data that is collected. For example, Cytel once worked with a client pursuing a Phase 2 study who had been asked to provide a natural history before proceeding. The collaboration of experts in real world evidence, study design, and data management, ensured that the data collected for the natural history study could be re-used as a comparator arm for the Phase 2 study.
For these seamless studies, data management can be just as much of a complicating factor as types of data collected. The manner in which data is collected, stored, analyzed, and thereafter submitted, can be quite different throughout these various studies and streamlining them is a challenge of its own. For example, The way in which data is stored for natural history studies that need to be reused for synthetic or external comparator arms sometimes requires special data management expertise, as the comparative study eventually becomes a single arm study rather than an RCT. Carrying out such a novel design requires additional expertise in the area of data management.
Furthermore, the purpose of natural history and comparator studies are quite different. Regulators are looking for different insights, and hence different processes for data management and storing might yield better results in each case. One of the benefits of working with a team that is strong in both biometrics and data management is the possibility of process-optimization, which enables data to be repurposed and optimized without having to reorganize a large study dataset.
Access and Transformation of Real-World Data
Navigating the field of real-world data can be complex. Cytel simplifies the process by ensuring that we ask the right research questions with the right data for the right analysis. We enable our clients to present an appropriate response to the Regulator’s question and smooth their path to accelerated approval.
As a pioneer in evidence generation, with deep expertise in advanced analytical solutions, Cytel is uniquely equipped to unlock the value from increasingly complex data. Life sciences companies count on Cytel to deliver exceptional insight, minimize trial risk, and accelerate the development of promising new medicines that improve human life.
Contact us to learn more and schedule a meeting with our experts.