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The Heart of the Matter 7: Reality Dawns

Forget zettabytes, Big Data is the stuff of a billion sound bites. As the CEO of a global technology company recently said: “Data is the new science; Big Data holds the answers.”
The heart of the matterForget zettabytes, Big Data is the stuff of a billion sound bites. As the CEO of a global technology company recently said: “Data is the new science; Big Data holds the answers.”

It’s a wonderfully hyperbolic one-liner and it may even be true. However, it only tells half the story. Because even with Big Data you can still be stuck for answers. In reality, Big Data is just that: data, and lots of it. The challenge is to turn that data into information and information into insight. For global biopharma, the ability to transform data into meaningful evidence can play a major role in brand differentiation and improving patient outcomes. But how can you turn Big Data into high-value Real-World Evidence (RWE)? It’s all about making the right connections.

Drivers for RWE
The drivers, benefits and value of RWE are now unquestionable. From a regulatory perspective, the Food and Drug Administration (FDA) has been requesting more post authorisation studies to monitor what happens with drugs in real-world settings since the FDA Amendments Act (FDAAA) was signed into law in 2007 clarifying the FDS's authority to require and enforce Risk Evaluation and Mitigation Strategies or REMS programmes. Likewise, European pharmacovigilance legislation implemented in 2012 means the European Medicines Agency (EMA) is now requesting post-marketing observational studies as part of its approval processes. In tandem, as the limitations of the 'gold standard' randomised clinical trial (RCT) data are increasingly exposed, European initiatives such as the Innovative Medicines Initiative GetReal and ADAPT-SMART underline how governments and regulators are striving for consensus on the use of observational research and iterative evidence generation that may result in patients gaining faster access to novel therapies.

As patient-centricity becomes embedded as a key priority for payers and providers, greater emphasis is being placed on real-world data and how it can be used to measure clinical outcomes and healthcare expenditure. In response, biotech and pharma companies are focused on developing data-led value propositions to generate evidence of budget impact, burden of illness, disease prevalence, treatment patterns, treatment response, comparative effectiveness and patient-reported outcomes. These are the preserve of RWE and their 'real-world' value is now indisputable.

In simple terms, the global need for effectiveness data to support brands' value propositions is writing a cheque that RCTs alone cannot cash. RWE has therefore become more than just a zeitgeist for pharmaceutical strategies - it's an essential ingredient in their success.

The evidence is there
The data to support the needs of the changing environment is undoubtedly out there. The challenge is to leverage and optimise it in ways that unlock the demonstrable value of your drug. And it's actually not as difficult as you might think. “A huge amount of real-world data is already collected in routine clinical practice - and there's a real opportunity to use it to demonstrate effectiveness in the real-world setting,” says Mariam Bibi, director of realworld evidence, Complete True Life. “But the true value comes when you look at multiple data sources throughout the patient journey to generate your evidence.

“In many cases, the effective integration of data from sources such as EMR (electronic medical records), claims databases or national repositories can uncover rich insights that can be used to demonstrate the real-world effectiveness of your product. By examining these datasets, identifying which endpoints they capture and establishing whether those endpoints can answer your research question, it becomes far easier to build a platform for meaningful RWE generation.”

Current observation
The most proactive pharmaceutical companies have recognised the importance of observational research and are building infrastructure and capability to facilitate it. A good example of this is Amgen, which established its own Centre for Observational Research (CfOR) in 2010 and continues to invest in its development. “CfOR focuses on our real-world data strategy, pooling experts in epidemiology, pharmacoepidemiology and pharmacovigilance to support a range of activities,” says Alex Liede, director, CfOR, Amgen. “The Centre has four areas of focus; forecasting, clinical development & RCT design, risk/benefit evaluations and pharmacovigilance. Non-interventional research in these key areas enables us to validate our understanding of the size and nature of our patient populations, measure patterns of care to guide the design and execution of our clinical programmes, inform our discussions around value and access, and develop programmes that accurately demonstrate the appropriate use – and safe use – of our therapies in the real world.

“An important milestone in the development of CfOR has been our recent move to a new data model. Our current efforts are focused on converting data to a common data model so that we can develop standard programs to analyse different data sets in the same fashion. Each data set will, of course, have its own strengths and limitations, but by organising data in a more uniform way we can integrate, interrogate and interpret analyses much more effectively. This has huge benefits for our research capabilities."

Making the link
Advances in technology mean that multiple data sources – from primary care, secondary care, laboratory test results, devices and wearables – can now be linked more easily. But experience and expertise is critical. There are two primary considerations when linking data; the challenge of accurately linking matched records, and then, once the link has been made, ensuring the data is securely stored and transmitted.

“There is complexity in ensuring that links which are made do in fact relate to matched records” says John Donno, group IT director, McCann Complete Medical. “Deterministic and probabilistic linkages can be used to ensure that the linked data is as accurate as possible. However, widespread variation in data quality, capture and structure means that linking requires both technical expertise and specialist healthcare knowledge. Human errors, missing data, variable identifiers and poor basic data quality can all present practical issues.

“Once linked, data security becomes critical. There have been many high profile examples of data loss. Companies need to have robust processes to ensure secure storage and appropriate transmission. Audit and redundancy mechanisms also come into play. These important aspects are best managed by third party partners with expertise that bridges technical and health-specific intelligence."

Real-world success
The benefits of linking data to create RWE are substantial. "At Amgen, our CfOR infrastructure has helped us use RWE to achieve orphan drug designation. Our study around hypercalcemia in cancer patients took a purely epidemiology - based approach to generating evidence - and became the first example of its kind to achieve a positive ruling from the FDA using linked EMR and real-worlding data. We've subsequently replicated the study in the UK using CPRD data," says Alex. "Similarly, we also leveraged real-world historical comparator data to mazimise a first-to-market opportunity with a breakthrough therapy designation, priority review and orphan drug designation for another rare tumor type. The FDA recently showcased this work as an example of how they welcome non-interventional data to supplement filings and accelerate approvals and access."

So how do you make the journey from Big Data to RWE? "Primarily it's important to understand your data sources," says Alex. "Real-world data can often be messy, disparate and full of gaps. Just getting a handle on the strengths and limitations of that data source is a crucial first step. Secondly, it's important to curate and organise data in a way that's ready for analysis. This will allow you to control any limitations or bias that exists in the data. The final step is the interpretation of data and sharing insights with your stakeholders. That's when it truly becomes real-world evidence. Everything prior to that is just data.

"To make the most of the opportunity, it's important to take a collaborative, cross-functional approach. A good partner cannot only help develop solutions to improve data quality, but they can work collaboratively as co-investigators to provide intellectual insight, build the analysis plan and interpret findings. The best research partners can also help identify the right data sources to answer key questions and - crucially - encrypt and link data on the company's behalf so that we're never exposed to patient identifier information"

Better connected
Developing an optimal RWE strategy is multifaceted and requires a truly integrated approach - both in terms of data sources and connecting internal and external stakeholders. In the modern environment, managing the transition from Big Data to RWE is likely to become a crucial determinant of brand success. But in the spirit of making the right connections, linking up with the right partner to help you optimise the opportunity will be similarly critical.

"The value of Big Data for outcomes research is undoubtedly exciting. However, it's important to understand that not all data sources are equal. Careful consideration needs to be given to the research question, the quality of the data source, the data governance and ethical requirements for access and where applicable, linkage. In addition, the use of RWE should be pro-active - leveraging data sources to support value demonstration across the health economy," says Mariam. "In all of these areas, the support of a trusted partner can add true value for brand teams throughout the product lifecycle. The best partners will be those that combine specialist healthcare intelligence and technical expertise. What's more, they'll have the infrastructure and multidisciplinary resources to develop integrated solutions at speed and scale.

"The future of generating effectiveness data in the modern marketplace will depend on companies' ability to leverage Big Data and transform it into relevant real-world evidence. It's all about making the right Connections."

Key Takeaways
  • HTA Bodies and regulators are placing an increased focused on value - looking at clinical and cost effectiveness in the real-world setting
  • Translating data to evidence and communicating this to relevant stakeholders are the key ingredients for a successful Real-World Evidence (RWE) strategy.
  • Take a collaborative, cross-functional approach, combining technical expertise with healthcare knowledge
  • Identifying evidence gaps and plugging them with RWE is vital for commercial success
  • RWE comes from multiple sources and audiences; to maximise the value of RWE it is important to understand the nuances of each data set


  The Heart of the Matter 7: Reality Dawns
PDF File: 281.5 KB

13th June 2016


  The Heart of the Matter 7: Reality Dawns
PDF File: 281.5 KB


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McCann Complete Medical

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