An increasing need for real world evidence (RWE), the associated rising cost of conducting clinical studies, and a demand for faster results, are driving forces for innovation. Providers, patients, and life science companies are finding new and better ways to capture real world data (RWD), and to leverage technology to generate deeper, broader, and more reliable insights in less time and at a lower cost.
However, to generate the most value from RWE, life science companies need to understand which RWE study designs are most appropriate and most fit-for-purpose, and how to ensure they deliver reliable, unbiased results. Discussed below are some of the most innovative, cost- effective solutions to generate robust RWE.
Pragmatic trials evaluate the effectiveness, safety, and/or health-economic benefits of a new or existing treatment in routine care. They are conducted in real world care settings, such as clinics, hospitals, and health systems, and involve diverse, representative populations. They are the design of choice to evaluate effectiveness of treatments in routine care, particularly where there are differences in formulations, and/or frequencies of dosing and blinding or masking of treatment is not desirable.
One of the most appealing features of a pragmatic trial is that it achieves the objectivity of a clinical study by randomizing patients to treatment assignment. As a result, this study design offers a reliable and cost-effective way to demonstrate the real world impact of marketed products without the cost and burden of a traditional, randomized clinical trial (RCT).
In one example, the U.S. Food and Drug Administration (FDA) approved the inclusion of real-world data in product labeling for the antipsychotic INVEGA SUSTENNA based on pragmatic trial results. The study showed that patients who were randomized to an injectable, long-acting form of the drug experienced delayed time-to-treatment-failure compared to the most commonly prescribed daily oral treatments, resulting in significant health cost savings, reduced hospitalizations, and fewer long-term stays1.
External comparators captured from RWD help establish comparative context for clinical research. They are particularly valuable for single-arm trials, where control arms are not ethical or feasible due to rare or small patient populations, and/or the progressive nature of the disease. External comparators can also serve alongside single-arm treatment registries to provide context for post-approval safety studies.
In these studies, a cohort of patients are identified from RWD who mimic RCT eligibility criteria to add comparative context to clinical research. Health data may come from electronic medical records (EMR), patient registries, claims, or other sources.
The use of external comparator studies to support approval of single-arm trials has increased significantly in the U.S. In one example, Bavencio received accelerated approval from the FDA and the EMA for the treatment of metastatic Merkel cell carcinoma based on the pivotal results of the JAVELIN Merkel 200 study, which showed patients receiving Bavencio had a 33% response rate, in contrast to two different real world benchmarks showing a 10% response rate in a European registry and 29% from US oncology EMR data2.
Innovative Extension studies are useful to measure longer term effectiveness and/or safety outcomes among patients who have participated in a clinical trial. The goal is to continue to observe patients after the completion of the clinical trial (or several trials) and capture long-term outcomes based on prior or ongoing exposure to the product(s). This approach can be used to simplify long-term treatment of trial patients before a drug is commercially available, monitor for long term patient benefits or risks, and reduce the cost and burden of long-term follow up studies.
Innovative Extension studies often use direct-to-patient data capture strategies to reduce or eliminate the need for physician and site involvement. This direct-to-patient approach can ease the burden on sites and investigators, and make the study less costly as a result of lower site burden.
Enriched studies use existing healthcare data coupled with selective prospective data collection on the same patient for robust, patient-centered insights into real-world experience.
Enriched studies integrate primary and secondary data at the patient level to build a comprehensive patient record. The primary data addresses the specific study objective and may be gathered through electronic case report forms (eCRF), PROs, and/or devices. Then, those insights are integrated with data gathered from electronic medical records (EMRs), claims reports, registries, and other secondary sources.
This is a useful model for meeting post-approval evidence needs because researchers can prospectively collect data that is not readily available from secondary sources, while leveraging available health data where possible. This approach increases study value by gathering clinically rich data on diverse subgroups with less site burden.
These novel study designs and data collection methods promise to transform the pharmaceutical research environment with patient-generated data. Going forward, we expect to see continued evolution of these RWE methods through the use of advanced technology and direct-to-patient data collection strategies. These cost effective solutions allow researchers to generate more comprehensive RWE in a timely manner.
IQVIA can help you analyze and understand the ever-expanding ecosystem of clinically-rich data in the context of your organization’s needs. By combining advanced analytics with unparalleled data and scientific expertise, IQVIA provides customers with innovative real world evidence approaches to meet stakeholder needs throughout the product lifecycle.
1 Alphs L, Benson C, Cheshire-Kinney K et al. J Clin Psychiatry 2015; 76(5): 554-561.
2 Becker JC, Lorenz E, Ugurel S, et al. Evaluation of real-world treatment outcomes in patients with distant metastatic Merkel cell carcinoma following second-line chemotherapy in Europe. Oncotarget 2017; 8(45): 79731 – 79741.