Real World Evidence: Making RWE Real


Can real-world evidence and advanced analytics accelerate the evaluation of drug safety and efficacy?

When the 21st Century Cares Act became law in December 2016, it ushered in a new era for real-world evidence (RWE) to help break bottlenecks in drug development and product approvals. The door has been opened, as drugs and medical devices are developed, to make use of vast amounts and divergent sources of health-related data:

  • • Claims data – medical and pharmaceutical
  • • Clinical trials data
  • • Clinical setting data – from electronic health records and lab results to genomic    or  pathology reports
  • • Pharmacy data – point-of-sale and Rx-fill rates
  • • Patient-powered data – self-reported outcomes, social media 

The Cures Act excludes randomized clinical trials (RTCs) from its definition of RWE – defining it as “other than” RTC data with regard to evaluating “the usage, or the potential benefits or risks, of a drug.” The New England Journal of Medicine says RWE is “health care information from atypical sources,” which includes billing databases and product and disease registries. 

The U.S. FDA needs to establish some guidelines for what it considers real-world data (RWD) and how it will allow RWE to be used. In July 2016 the agency introduced draft guidance relative to RWE and medical devices. The 21st Century Cures Act has given the FDA some timelines for drafting guidance relative to using RWE in these instances:

  1. to help support the approval of a new indication for a drug approved under section  505(c) 
  2. to help support or satisfy post-approval study requirements

An openness to RWE indicates recognition of the usefulness of data generated from actual use in a clinical setting. The goal is to ensure that relevant RWE data is applied methodically to the evaluation of drugs for safety and efficacy. 

The Promise of RWE

RWE provides a more comprehensive view of a drug product’s real-life therapeutic and economic value to patients, payers, providers, and sponsors.  It adds real-life clinical practice and actual health outcomes information to our understanding of drug therapies, and is being eyed for its potential in expanded labeling and repurposing of existing drugs. It can help us study physician utilization patterns, the patient treatment journey, and drug comparative effectiveness.

AstraZeneca, for example, used RWE studies to supplement RCT data in a 2013 study of COPD treatment. Consider this data pool:

  • • medical records of 21,361 patients over an 11-year period
  • • linked national, mandatory Swedish healthcare registries – including hospital, drug,  and cause-of-death data
  • • total anonymized data representing 19,000 patient years

The benefit to AZ and COPD patients? “By combining such large quantities of data with appropriate statistical techniques, the study gives healthcare providers a fuller picture of how COPD care has evolved and the impact of different COPD management strategies on outcomes for patients in actual clinical practice,” wrote AZ’s Georgios Stratelis, MD, PhD, in a blog post. 

From aiding regulatory approval decisions for new products, generating ideas for next products, providing longitudinal assessments, to proving post-market value, RWE offers considerable promise for pharmaceuticals.  It is said to provide as much as $450 billion in top-down opportunity for U.S. healthcare alone. 

Overcoming RWE Challenges: Advanced Analytics

Until the FDA establishes its guidelines and the global industry settles upon agreed standards for RWE use, various participants and stakeholders are working to clear hurdles and eliminate obstacles. Among those is the data challenge itself – data quality.

The Network for Excellence in Health Innovation (NEHI) hosted a roundtable in December 2014, “Real World Evidence: Ready for Prime Time?” that cited data quality as the top barrier to the use of RWE. “Most sources of RWD [real-world data] are not collected for research purposes. Many researchers become ‘data janitors,’ forced to ‘clean’ gaps and inconsistencies in data through methods that may not yet have wide acceptance for statistical validity,” the report states.

In addition to data sources like insurance claims, electronic health and medical records (Health Economics and Outcomes Research), and pharmacy bills, RWE can also include:

  • • radiographic images
  • • biobank data
  • • molecular genomic data
  • • vital statistics
  • • patient wearable-generated data

While exciting, this adds significant new volume (on top of already crushing data loads), data integration challenges, and a real need for long-running analytical and visualization capabilities. Unified access to all relevant data sources empowers scientists and others to make decisions based on the most comprehensive dataset, including RWE data. Technology platforms must be able to integrate and make sense of RWE data, in near-real time, for decision makers to make productive use of it. 

Accelerating time-to-insight is a top goal, and achievable with out-of-the-box RWE solutions offering pre-built analysis modules for:

  • • cohort-building with propensity score matching
  • • comparative effectiveness
  • • safety signal detection methods
  • • machine learning

Learn more about PerkinElmer’s solutions for integrating RWE and leveraging all data to accelerate drug development and get therapies to market faster.

Can real-world evidence and advanced analytics accelerate the evaluation of drug safety and efficacy?We’re convinced it can. Download our white paper, Real-World Evidence Through Advanced Analytics, for a more complete analysis of the challenges – and opportunities – at hand.



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