We’ve all experienced the value that innovation has brought to our lives. The cloud, enormous data sets and more accurate AI modeling have enabled many organizations to bring new products and services to market in ways we could not have imagined 20 years ago.
The health care and life sciences sectors are no different. An expanding universe of data is being brought to bear on our most pressing health needs. Covid-19, cancer and heart disease – big killers all – have been made a little less lethal. Certainly, hard work remains to tame these diseases, but we are finding more and more pieces to these complex and puzzling illnesses in the data.
Whether it is data gathered in well-designed trials or in clinical settings, real-world evidence is helping researchers and clinicians better evaluate drugs, design devices and devise more targeted treatments. Even data from wearables is finding a growing role in health care.
There are a few important ways this data is being used – to accelerate and validate medical advancements, to evaluate the effectiveness of therapies and one of the most important, to better target specific patient populations.
Fit-for-purpose data leads to more effective therapies
In a presentation at SAS Global Forum, epidemiologist Brian Bradbury, VP of Center for Observational Research at Amgen, said that his company is using real-world evidence (large health care data sets) to determine is not only the safety and effectiveness of their medications, but whether these drugs are being administered as designed in clinical settings.
Using analytics, Amgen can uncover crucial answers in a short period regardless of how large data sets might be and provide regulators with the information they need to make a decision.
How does this work in practice? Amgen conducted a study in which it followed patients for 10 years who were suffering from a variety of tumor types to evaluate the effectiveness of Amgen medicines. What it discovered was that when medication levels dropped the use of blood transfusions increase (something you want to avoid). The evidence they discovered in the data now can be used to evaluate treatment over time.
Analyzing evidence to discover a modus operandi
Another example of the use of real-world evidence to help streamline drug approval might be to offer national regulators from one country a clinical data set that might be applied to approval of the drug in another country with a similar population.
Amgen has a drug that had been approved in several countries and was seeking an additional approval from Chinese regulators. It was able to provide the regulators with lots of data regarding drug effectiveness among other ethnic Chinese populations in Asia. The data showed that the drug was highly effective in people of Chinese descent. Ultimately, this evidence played an important part in gaining approval for use in China, according to Bradbury. “We had a very targeted question in mind and with appropriate epidemiologic design and analytic techniques, we were able to provide evidence to enable a regulatory decision, which we thought was really critical for our mission, which is to serve patients,” Bradbury said.
RWE: The near future
RWE is continuing to show its value as a critical proof point that can enable more informed regulatory, reimbursement and provider decisions and as a guidepost for the design and application of clinical trials in support of treatment prioritization.
“Greater transparency and fit-for-purpose data are really helping to pave the road,” Bradbury said. “It's the intersection of those dimensions that really help us to advance. And certainly we are going to continue pushing for data quality standards because we know that it's the quality of the data that we integrate into our analyses that really helps dictate whether the end results will be garbage in, garbage or better data quality. Advancing our data quality standards and bringing that to bear with the right design and approaches can really help us generate real world evidence that can have a meaningful impact."
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