In this real-world evidence blog post, Worldwide Clinical Trials discusses the new, viable approaches to improving medical decision-making that are at the forefront, in addition to making the case for “observentional” research.
Real-world evidence (RWE) and real-world data (RWD) are getting more attention today, as viable approaches to improving medical decision-making are at the forefront. In stark contrast to the artificial, experimental context underlying randomized clinical trials (RCTs) (which may have limited applicability to actual patient care), capturing information reflecting standard medical practice and outcomes is the modern-day practice. However, expectations may be ahead of reality for both the acceptability of accessing and operational efficiency of generating RWD.
Real-World Data: Expectation vs. Reality
The expectation that quality and comprehensive RWD simply relies on a simple database query denies the reality of incomplete and inconsistent datasets. While technology and data integrity are improving, and RWD are being explored to address needs across the development and commercialization continuum, there remain important gaps (e.g., systematic inclusion of patient-reported outcomes). That said, a quick retrospective look at data to characterize the landscape of a disease and/or the utilization of a specific product may be the truest reflection of what is actually happening in the real world. This runs counter, however, to our general expectation that prospective research is always better.
Making the Case for “Observentional” Research
A key advantage of prospective observational research – a rightful mainstay for the generation of RWD and RWE – can be the a priori specification of data to be collected, thereby maximizing the potential for examining treatments, processes, causality, outcomes, etc. The ideal observational study merely looks “over the shoulder” of patients and physicians and does not affect the standard of care delivered (importantly, because that’s what’s being studied in the first place).
But is there ever really a prospective observational study that doesn’t have some impact on what’s being observed? Consider the following:
- A set of case report forms that, while seemingly benign, inadvertently change physician behavior to be more “outcomes-oriented”
- Inclusion of physician and/or patient-reported assessments (e.g., quality of life) that aren’t normally undertaken as the physician’s standard of care
- Collecting blood and tissue samples for biomarker analysis within the context of an otherwise non-interventional study
One could argue that even the act of consenting a patient as part of an observational study can have an impact on the overall care or outcomes of the patient. Thus, we’ve coined an entirely new phrase for prospective observational studies that may contain components with interventional impact: “observentional” research.
Tongue-in-Cheek or Reality?
The phrase is employed largely to underscore the reality that the observational study may not perfectly reflect actual practice and to acknowledge that certain supporting operational processes (e.g., ethics and regulatory approvals) may lie somewhere between those appropriate for observational studies and more traditional interventional trials (e.g., RCTs). In addition, the implications for observentional research range from establishing appropriate site honoraria to the more significant (and often positive) impact on patient outcomes.
A Platform for Success
In our experience, foresight and precise communication are of paramount importance. Ensuring that the expectations of all parties – sponsors, CRO study teams, investigators, patients, external review boards, contracting bodies – are appropriately and insightfully determined up-front can avoid the surprises and pivots necessary when the operational implications of a seemingly benign observational study are revealed.
Where Do We Go From Here?
Although we don’t expect our terminology to find its way into the day-to-day research lexicon, we know that observentional research continues to be a useful departure from the norm and, as such, helps when determining the impact of an observational study that wasn’t as observational as expected!
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