Use of real-world evidence for regulatory decision-making

How do you evaluate treatment efficacy and safety outside of the clinical trial setting? This is not just a question of academic interest. In last week’s JAMA, Rubin 2021 writes about some of the challenges of evaluating COVID-19 vaccines outside of a clinical trial setting.

In an interesting, two-day workshop last week, the Duke Margolis Center for Health Policy attempted to begin to answer this question. The title of the workshop was “Evaluating RWE from Observational Studies in Regulatory Decision-Making: Lessons Learned from Trial Replication Analyses

While there was a lot of content covered, there are three main points I’d like to highlight: (i) use caution when dealing with prevalent rather than incident users, (ii) increase transparency, and (iii) RWE and RCT results may differ even if RWE results are valid.

Using incident vs. prevalent patients

In a typical clinical trial, individuals are assigned to different treatment arms and given the relevant medication–typically either the treatment or the placebo. In the real-world, however, one simple way to create “arms” is to assign people who are using the treatment to one arm and people who are using the treatment to another arm [this “assignment” is done in an analytic sense, not in the real world]. If one examines looking at people who initiate the treatment, the real-world data can be very useful. One must consider whether there is systematic bias in how doctors are prescribing the treatment, but based on Miguel Hernán‘s talk on Day 1 of the webinar, identifying incident patients are key.

Another approach would be to compare people using the treatment compared to those who are not, but not requiring them to be incident users. The problem with this is approach is that the sample of prevalent users becomes more biased over time. Let’s say that the new treatment works well for half the users and poorly for the other half. It could be possible that the people for whom the treatment did not work would stop using it. Thus, you would only be left with individuals for whom the treatment worked. Thus, the sample would be biased and you would overestimate the health benefits of initiating treatment.

Thus, two tips for researchers would be: (i) focus on incident users, and (ii) identify the potential direction of any bias in treatment assignment of incident users and conduct sensitivity analysis to try to bound this bias.

Increased transparency

When clinical trials are done, the are typically registered at ClinicalTrials.gov. For real-world studies, authors should also publish their study protocols ahead of time and document any deviations from said protocols. Doing this will not only increase the transparency of how real-world evidence is being used, but will also increase the credibility of the study. There are a number of efforts such as the RCT Duplicate, the Observational Patient Evidence for Regulatory Approval and uNderstanding Disease (OPERAND) Project, and efforts by the Yale University-Mayo Clinic’s Center of Excellence in Regulatory Science and Innovation (CERSI) project.

Why RCT and RWE studies may vary

On Day 2 of the workshop, Michele Jonsson-Funk of the UNC Gillings School of Public Health provided a nice overview of some common reasons why RWE and RCT results may differ even if the RCT results themselves are not biased.

  • Random error. The first one is the most obvious. Even if the causal parameter of interest in the population are identical in the RWE and RCT, it could be the case that they differ in the samples collected just due to statistical error and random noise. For instance, RWE studies often have larger sample sizes and the causal effect may be more precisely estimated (if the RWE study design is well done).
  • Answering different questions. In RCTs, one may be able to estimate the causal effect on the population of interest. In RWE, uptake of the new treatment may be more common for a specific subgroup. Thus, RCTs may often answer the causal effect for the population as a whole whereas the RWE data may be able to estimate the treatment effect on the treated. This later point is a completely valid causal estimate, but one must note that it does differ from the causal effect for the population as a whole.
  • Different baseline hazard rates. In clinical trials, individuals with multiple comorbidities are often excluded whereas that is not the case in the real-world. On the other hand, some trials focus on later lines of therapy and individuals may have higher baseline hazards. Either way, the baseline risk–and thus baseline number of bad events (e.g., deaths, hospitalizations) may differ across the trial and real-world population. Even if the relative treatment effect is identical in the trial and real-world, the absolute impact will differ; or it could be the case that the absolute impacts are similar but the relative impacts would differ. Either way, different baseline event rates will complicate direct comparisons between trial and real-world data.
  • Different treatment. In the clinical trial, dosing is done very systematically. In the real world, dosing may be more flexible (e.g., patients may switch therapies more often). Thus, the “treatment” being evaluated in the trial and real-world may differ across the settings. For instance, in the COVID-19 vaccine trials, second doses were given shortly after the first; in the real-world, many countries are postponing second doses.
  • Outcome differences. Real world data may have much longer (or shorter) follow-up compared to the trial. Further, if one is using claims data, people can disenroll in health insurance for reasons that may or may not be related to the treatment of interest; disenrolling in a clinical trial may occur for very different reasons. Dr. Jonsson-Funk also noted the issue of competing risks may differ in the trial compared to real-world as well.
  • Adherence differs. As is well known, treatment adherence is typically much worse in the real world. Patient often have cost-sharing burdens, visits to the clinic are less frequent, and the motivation of real-world patients may be lower than those in clinical trials. My own research notes the low real-world adherence levels, particularly for patients with multiple chronic conditions.

The workshop videos are now being posted online and I encourage those of you interested to take a look. Interesting throughout.

Pulse Infoframe Releases Registry for Patient Advocacy Groups

Launching a rare disease patient registry often requires patient advocacy groups to design a registry themselves or pay for an expensive customized solution. With the launch of Rare Central™, Pulse Infoframe offers patient advocacy groups an on-ramp to collecting real-world data, including natural history data, disease-specific data, and patient-reported outcomes. As a patient group’s needs grow and evolve over time, Rare Central’s three levels of entry offer partners the ability to scale data collection to support a wide range of research objectives as their needs become more complex.

Dr. Femida Gwadry-Sridhar, CEO of Pulse Infoframe, says, “Patient groups are experts in their disease and are at the forefront of grassroots operations. They’re best placed to co-design evidence generation solutions that are meaningful to their communities.”

Rare Central was purpose-built for patients and patient advocacy groups. It provides a cost-effective, real-world data collection solution for patient groups looking to accelerate global research into therapeutic treatments for rare diseases.

Collaboration: The Foundation of Rare Central

Alongside its support for research carried out by patients, advocacy groups, pharmaceutical companies, and academic medical centers, Rare Central also enables scientists and researchers to study deidentified data across a family of diseases with the appropriate governance structure and data sharing in place. By leveraging a shared data infrastructure, Rare Central gives researchers data that can be used to advance treatment options within and across therapeutic classes.

Because of the financial and time constraints smaller advocacy groups face when trying to establish registries, they are often excluded from the larger research picture. This makes it difficult to compete with other organizations who are campaigning for researchers and funders to help them discover life-changing treatments.

Rare Central aims to change that, because it can connect patients around the world.

“This makes it a viable option even for smaller patient groups who represent some of the rarest diseases in the world and have been historically unable to commit to a registry,” says Gwadry-Sridhar. “And as the needs of these small groups change, Rare Central is designed to change with them.”

How Rare Central Works

Rare Central is split into three tiers patient advocacy groups can move between at a pace that suits them. This allows organizations with little knowledge of registries to begin collecting quality, regulatory-grade, real-world data at a pace they can manage and then grow as they learn and as resources become available to them, without moving to a new platform.

The Starter tier is perfect for groups beginning their registry journey. It helps establish an initial patient registry on a limited budget, including a contact database, diagnostic information, quality of life surveys. It can help administrators monitor recruitment progress, and patients can track how they are doing.

The Accelerated tier is ideal for organizations supporting a broader set of patient-reported and disease-specific variables, including natural history data. Researchers can use additional tools, such as pre-defined reporting and demographic query functionality, to dive deeper into the rea-world data they have collected via Rare Central Accelerated.

The Advanced tier supports organizations with a need to combine patient-reported data with data entered by clinicians. With a broader set of variables Rare Central Advanced supports multi-site and disease-specific patient registries and natural history data. Researchers can use the information to help better target drug development and to support clinical trials.

A Decade of Experience

Gwadry-Sridhar founded Pulse Infoframe to fill a research gap that neglected millions: an inability to collect standardized, useful real-world evidence in rare disease, cancer, and chronic condition populations. Since 2011, the company has been designing solutions that accelerate rare disease research and create lasting inter-sector partnerships across the globe.

To date, Pulse Infoframe has supported the launch of 70+ registry sites and data hubs, covering more than 25 diseases, working with 10+ industry partners, and supporting the writing of 220 peer-reviewed publications.

Collecting sensitive medical data requires a platform that follows stringent regulatory guidelines. Pulse Infoframe’s solutions comply with all necessary regulations so patient advocacy groups can collect patient data securely, no matter where in the world the patients live.

Patient Advocacy Groups Have a Partner with Pulse Infoframe

Education and support are needed to make disease registries successful. When patient groups work with Pulse Infoframe, they have access to in-house experts who specialize in these areas:

  • evidence strategy and analytics
  • patient insights and engagement
  • patient-reported outcomes
  • language services
  • strategic regulatory services
  • pharmacovigilance
  • risk management

In other words, Pulse Infoframe provides patient groups’ registries the best chance of having real influence on research. Additionally, Rare Central addresses the needs of patients by providing an easy-to-use interface that simplifies the data input process. This, which has benefits for patients with motor skill issues. Lastly, and the patient dashboard allows them patients to track their condition over time.

Rare Central Also Benefits Researchers and Sponsors

For medical facilities and researchers, working with groups using Rare Central brings with it the ability to develop biomarkers and endpoints such as treatment effectiveness and symptomatic events. In addition, researchers can develop evidence to support further research and publications.

Rare Central also benefits sponsors and the wider pharmaceutical industry. For example, it can help increase recruitment for clinical trials, support identification of molecular compounds, and allow sponsors to leverage natural history as comparator arms for platform trials. Pulse Infoframe’s expert in-house teams will also provide additional support with marketing and public relations.

A Special Program for Patient Advocacy Groups and Foundations

Pulse Infoframe is currently seeking to partner with an initial group of patient advocacy groups and research foundations. The early adopters participating in the Rare Central Pioneer Program will receive waived or discounted fees for the first year along with training and support from Pulse Infoframe’s in-house experts. During this year, each group can begin building a portfolio of evidence that can attract new stakeholders and funders to support expansion.

In 2021, Pulse Infoframe heads into its second decade with a focus on expanding their real-world data collection efforts in 20 rare diseases, across four rare disease families. For more information on Rare Central and Pulse Infoframe, visit www.pulseinfoframe.com or email [email protected].

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Did your real-world study follow STROBE guidelines?

Is your observational research study following best practices? Is your methodology transparent? To help answer these questions, the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) Network created the STROBE guidelines. The STROBE guidelines–an acronym for The Strengthening the Reporting of Observational Studies in Epidemiology–aim to improve the transparency of the methods behind observational research studies. The full guideline checklist is here, and I provide an overview an commentary below.

The Introductionsection should clearly explain the current literature, the gap in the existing literature, and the objective of the study.

The Methods section needs to describe how the study was conducted. Specific key elements include:

  • Study design: Is this retrospective of prospective? Cohort, case control or cross sectional?
  • Setting: Which country? Which care setting? How were people recruited? Over what time period were outcomes observed?
  • Participants: Describe inclusion and exclusion criteria. If the study is a matching study (e.g., propensity score matching) describe how the matching was done.
  • Variables: Key variable categories include the outcome, intervention/exposures, and other independent variables (i.e., predictors, confounders, effect modifiers)
  • Data source(s): Describe the data source for each variable used in the study. Often times the setting and data source may overlap.
  • Sample size: If this was a prospective study, how was the sample size arrived at? Was there a power calculation? If this was retrospective, how big is the population from which the sample was drawn from?
  • Statistical methods: Describe how the impact of the exposure on the outcome was modeled. Provide justification for the approach. Describe any subgroups analyzed and justification for investigating said subgroups. Describe how missing data (if relevant) will be addressed statistically. Discuss any sampling strategy or re-weighting of the sample. Include a description of any sensitivity analyses.

The Results section should also be presented in a clear manner.

  • Participants. A flow diagram or table should be used either in the body text or in an appendix to show how the study inclusion and exclusion criteria affected the sample size.
  • Descriptive data. Researchers should provide basic demographic and relevant health information. For economic studies, baseline utilization and cost information should be shown. For cohort studies, typical follow-up time should be shown, particularly for unbalanced panels (i.e., variable follow-up time). In this section or in the outcomes section, the number of people per exposure group should be shown.
  • Outcomes (unadjusted). This section will compare outcomes between different exposure groups. This would be differences in outcomes before propensity score matching, or before regression adjustment, or before implementing an instrumental variables strategy. The goal is to show whether differences in the main results are due to raw differences or if the statistical analysis used changes the results. For time to event studies, reporting the raw number of events.
  • Main results. Show the main results. The structure of this section should parallel the structure of your statistical methods section.
  • Other analyses: Typically this section would have any subgroup analysis, sensitivity analysis, or falsification tests/robustness checks.

While the methods and results sections aim to clearly describe what was done, the Discussion section of the paper aims places the results in context and identify key limitations. There are generally four sections.

  • Key results: The first paragraph of the discussion summarizes the results.
  • Interpretation: This section describes how this result fits within existing literature. If the results are similar to those in other studies, that is helpful. If they are different, explain propose some hypotheses why they could be different
  • Generalizability: Authors should describe the contexts to which the study results are most versus least generalizable to give context to results.
  • Limitations. This section identify key study limitations. If the limitation affects generalizability, that should be stated clearly. If the limitation is likely to bias the results, it is helpful to provide a direction of the bias or try bound the magnitude of such bias. If other methods could be superior, the authors should clarify why these other methods were not selected for this study.

Other information should also be shared such as study funding, author disclosures, and acknowledgements. Where more detailed methods or results are needed, researchers should use supplementary appendices where available.

The STROBE guidelines are not perfect and are focused more on epidemiological than health economic studies. Nevertheless, these guidelines provide a helpful outline for how researchers should report the results of real-world, observational data studies in peer-reviewed journals.

EMA will use Panalgo software for real-world data analytics

Six months after rebranding from BHE, Panalgo has won a contract to supply the European Medicines Agency (EMA) with its IHD data analytics platform, pledging to streamline its public health efforts.

IHD – or Instant Health Data – will be used by the EMA to carry out data analyses and examine medicinal product utilisation, answer questions about safety and efficacy, and understand how treatments perform in real-world settings, said the Boston, US-based company.

The use of real-world evidence (RWE) has grown rapidly in pharma over the last few years, as healthcare payers and regulatory agencies have tried to get a deeper understanding of the impact medicinal products have in actual practice, rather than just within clinical trial settings.

“Our company is strongly aligned with the EMA’s public health goals, for which timely evidence-based insights are of particular importance, especially during this COVID-19 pandemic,” commented the US company’s CEO Joseph Menzin.

Panalgo is also opening an office in Amsterdam, the home of the EMA, in order to provide close support for the roll-out and management of the new software at the regulator.

Panalgo says IHD’s library of healthcare-specific algorithms does away with the need for complex programming and allows users to “focus on what matters most: turning data into insights”, using sources like electronic health records and patient registries.

There has been a spike in the use of data analytics by healthcare organisations during the coronavirus pandemic to try to guide the public health response to the crisis, for example to make effective, real-time use of resources.

The new agreement with the EMA adds a new dimension to Panalgo’s data analytics business, as most of its clients have so far been life science companies and research organisations.

The company says IHD passed a rigorous assessment by the EMA – in competition with software from other providers – and will support decision-making at the population level, including therapy risk/benefit and life cycle analyses that can be used in public health planning.

“Most of Panalgo’s life sciences clients already rely on IHD to perform rapid analyses that support regulatory initiatives,” said Menzin.

“The IHD platform will be a valuable tool to allow collaboration between drug manufacturers and regulatory agencies, such as the EMA,” he added.

Image by Ceescamel – Own work, via Wikimedia, CC BY-SA 4.0

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Harnessing The Power of Real World Evidence to Fight COVID-19 Pandemic

With the ongoing pandemic of COVID-19, there is long wait for the vaccine to be approved. Even after the vaccine gets approved, there is no assurance of the complete eradication of virus. There will still be percentage of people who contract the illness. Therefore, it is very important to trace and detect the treatments in varying populations. The healthcare system is required to gather data about patients as well as vaccine recipients and power of real world evidence could be critical in this respect. This will further assist in guiding treatment decisions, identify risks and inform standards of care.

In the ever-evolving healthcare market, both researchers and practitioners are increasingly adapting innovative methods to advance the quality of patient care and improve clinical outcomes. The healthcare systems that earlier focused on medical interventions driven by episodic interaction with the patients, are now recognizing the need to fully understand exogenous factors (such as genomics, behavior, and social and environmental) in order to deliver continual care. The holistic approach to healthcare system has been highlighted below, which briefs about the role of real world data in decision making.

This data landscape is continuously changing and the capacity for rapid data accumulation and interpretation is also advancing exponentially. A myriad of innovative techniques, such as computer learning and natural language processing (NLP), and the evolution of electronic health records (EHRs) are revolutionizing the availability and potential use of real world data sources to improve healthcare outcomes. These techniques are significantly contributing towards the development of COVID vaccine and understanding the novel virus.

A Real World Approach to COVID-19

According to USFDA, power of real world evidence uptake is “silver lining” of the COVID-19 response. In the long term, real-world data will assist in understanding the way virus impairs the body, new health risks it creates and emerging long term complications. The real-world evidence gathered from longitudinal studies of COVID-19 patients and vaccine recipients is likely to play a significant role in achieving these goals. As it has been observed by scientists about the heterogenous nature of the virus, the insights from the real world populations are critical. The data will certainly help in understanding the virus and further, assist the healthcare professionals to plan the specific treatment as well as minimize the risks of serious complications. In addition, real world data is likely to present the results of certain population categories, which have been excluded from the clinical trials. These categories include pregnant women, old age people as well as patients with existing comorbidities.

Earlier in 2014, during outbreak of Ebola, the forecasters used the combination of real world data of population and their mobility along with their rigorous random models of disease transmission. This further assist in the prediction of the status of spread of the disease, globally. In the similar manner, tracking models can be adopted to fight any upcoming COVID-19 outbreaks. As predicted by scientists, it can take more than one year for the COVID vaccine to reach the market. Therefore, in such situations, real world evidence can be proved to become an essential tool to suppress the COVID-19.

As mentioned earlier, data can be generated from various networks and further analyzed by the healthcare organizations. This will assist them in monitoring and controlling the real-time disease. In addition, the adoption of technology in such areas, will lead towards the automation and analyzing, which is the utmost requirement for the big data.

Further, technological advancements have made it possible in aggregating the data from various online platforms. These platforms include traditional reporting tools and mobile applications. There are various government-backed applications, which are analyzing the personal level information to group individuals in the form of color coded categories. This information can directly be utilized to check upon the status of health as well as risk to contract the virus.

Check out our new Reports Here-

Pharmaceutical and Life Sciences Real World Evidence: Market Landscape and Competitive Insights, 2018-2030

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FDA’s use of real-world data

The 21st Century Cures Act required the Food and Drug Administration (FDA) establish a program for evaluating the use of real-world data (RWD) to support the approval of new indications for drugs. Real-world data is typically data from either health insurance claims, electronic health records (EHRs), patient registries, or mobile devices. But how has FDA used RWD in practice?

A paper by Feinberg et al. (2020) examines oncology drugs approved by the FDA between 2017 and 2019 to try to answer this question. In this time period, 40 new oncology drugs were approved.

Five of the 40 made reference to RWE submitted in support of the approval. During the same time period, 71 supplemental indication approvals were identified (for 38 oncology drugs); however, drug approval packages were only available for 13. Three of the 13 made reference to RWE submitted in support of the approval. All 8 of the approvals with submitted RWE involved indications with an unmet need for effective therapies. For 5 of the 8 approvals with submitted RWE, the data represented historical controls; in 2 cases the RWE was derived from expanded access studies, and in 1 case the RWE was collected from off-label use of an approved therapy in a new patient population. The submitted RWE was rejected by FDA in 3 of the 8 approvals.

Many of the drugs using RWD were indicated for rare diseases and a variety of different real-world data sources were used.

…we found that 4 of the 5 drugs reviewed had orphan drug designation, and the fifth was for a rare subset within a larger patient population (palbociclib for male patients with breast cancer). Three of the 5 drugs (avelumab, blinatumomab, and selinexor) received accelerated approval for the indications for which RWE was submitted; all 3 had PMRs [post-marketing requirements] for confirmatory clinical trial data, with avelumab and blinatumomab both requiring new clinical trials. The types of RWE used in the regulatory submissions included EHR data, claims data, postmarketing safety reports, retrospective medical record reviews, and expanded access study data.

Efficacy was most often justified using EHR data as a historical control or expanded access studies. Sometimes the EHR data was supplemented with claims data or diagnostic test results (e.g., next-generation sequencing data).

Source:

  • Feinberg BA, Gajra A, Zettler ME, Phillips TD, Phillips Jr EG, Kish JK. Use of Real-World Evidence to Support FDA Approval of Oncology Drugs. Value in Health. 2020 Sep 14.

How real human experiences can power a healthier future

As part of our series of opinion pieces from leaders at Janssen, the company’s Maria Raad looks at how we can embrace tech and data science to overcome increasing pressures on healthcare systems.

For the next generation born in the western world, living to be the age of 100 will be the norm. While this seems like a desirable aspiration for our grandchildren, it adds new pressures on our healthcare systems. The number of people living with chronic illnesses will rise year-on-year, and the ongoing management of  illnesses like diabetes and cardiovascular disease will require ever increasing resources.

This is nothing new. Healthcare expenditure is growing faster than GDP the world over, a trend that has been amplified by the COVID-19 pandemic. Together, these forces have accelerated technological transformation, acceptance, and adoption across our industry, reinforcing my belief in the ‘triple aim’.

The triple aim considers how we balance the needs of the individual with pressures on our health systems and, in a phrase coined by Berwick, Nola and Whittington in 2008, it is defined as:

  1. Improving an individual’s experience of care
  2. Improving the health of populations
  3. Reducing the per capita costs of care

Changes to any one of these goals can affect the other two, negatively or positively. In order to succeed, we must shift the paradigm of healthcare and drive towards more objective measurements of value and improved experiences for everyone, to create a truly patient-centric system of care. ​

In the past, our collective adoption of, and trust in, digital technology has been incremental and often reluctant. All too often approached with a narrow mindset, such technologies have been regarded as simply tools to reduce costs or lessen the need for human interaction, when they have the potential to do so much more.

As the scientific evidence behind digital solutions has grown alongside our accelerated need for changes, we’re starting to see the full potential that data science might offer – a potential to create more efficient disease management solutions, reduce the economic burden of healthcare and, most importantly, empower patients to be integral decision-makers in their own care.

Real-time digital solutions for real-world evidence

Two billion people have access to mobile health data, but simply having access is not enough. It is how we use health data that will drive us forward. The emergence of big data, smartphone adoption and cloud storage technologies means that information can be captured in real-time, and then aggregated and analysed to develop new insights.

Collaborations such as HONEUR (Haematology Outcomes Network in Europe), which brings together a partnership of universities, hospitals and institutions across Europe, can help with this by bringing together multiple stakeholders to analyse real-world data (RWD), quickly and at scale, from as many sources as possible. By answering research questions in real time, partners can extract real-world evidence (RWE) that informs their conclusions and accelerates their work.

While acceptance varies between countries, we believe RWE is a key component in moving towards a more value-based healthcare model, as it is one of the few under-utilised resources left in our field.

Healthcare companies are now working with tech companies to put innovative, data-capturing tools directly in the hands of the individual. Wearable tech and mobile health apps can provide patients with information, allow them to manage medication, and help them to become experts in their own condition. This personal depth of knowledge can complement the quantitative data on which we currently rely, so we must ensure it’s integrated into treatment pathways going forward.

Combining digital therapeutics with pharmaceutical innovations

The pandemic has prompted a significant uptake in the use of data technologies. Clinicians, patients and payers are utilising the potential of these platforms, and many are doing so for the first time. We will, however, need to embed such changes across all parts of our healthcare systems, and I believe there are four foundational elements to this:

  • Evolving from sick-care to well-care: healthcare systems are struggling to provide care via traditional models, which are largely based on treating illness rather than preventing it. This means moving on from just products and treatments, to platforms and solutions focused on prevention and real-time, outcomes-based care. Digital technology will be a major contributor to this transformational shift from diagnosis and treatment to prediction and prevention.
  • Data science: harnessing data networks, artificial intelligence and real-world evidence, and the interdependence between all three, can help move us towards agreeing an objective measure of value for any given therapy. That’s essential if we are to build a new healthcare ecosystem, and if we get it right, everyone will benefit.
  • Long life care: ageing populations are growing in size and increasing the pressure on healthcare systems. Without digital technology, the amount of resources required to manage long-term, non-communicable diseases will be unsustainable.
  • Personalised care: digital therapeutics, when certified as medical devices, can enable clinicians to prescribe a treatment system that goes beyond the pill. They can also engage patients more effectively in their own care, through real-time symptom monitoring, for example, or by providing physiological support for those dealing with the burden of disease.

These are not short-term solutions. The ultimate goal is healthcare that’s thriving, sustainable and accessible to all. And we can drive towards that goal by harnessing and sharing the benefits of the ever-evolving technologies within our reach. But it’s a team sport – the days of any individual, organisation, government or industry attempting to change the world on their own are gone.

Data science actually has the potential to make healthcare more human. And, as we look to the next 100 years, with all this technology available to us, perhaps there’s reason to hope that we will yet see a world where fewer people get sick and more people live longer, healthier, happier lives.

About the author

Maria RaadMaria Raad is vice president, customer & digital strategy, EMEA at Janssen. In this capacity, Maria is responsible for the functions of business intelligence, advanced analytics, digital acceleration, and patient healthcare solutions. She has held various positions, since joining Janssen in 2006 – prior to her current role, Maria was Global VP & Chief Information Officer of Actelion, and a member of the Actelion leadership team based in Basel, Switzerland.

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Aetion completes $82M Series B round with $19M extension

The healthcare analytics company said it had raised the extension funding from three new investors and would use it to accelerate development of its real-world evidence technology.

5 Ways RWE is Fueling the Fight Against COVID-19

As we discussed in an earlier blog post, real-world evidence can bring value to every stage of the drug life cycle, from early discovery to post-market. Now, as the worldwide research community continues to battle the COVID-19 pandemic, we are also discovering how real-world data and real-world evidence have something to offer in these efforts as well.

With regard to
COVID-19, one of the key ways that RWE can help is in managing the outbreak.
RWE can assist researchers in monitoring the disease and understanding how it
is affecting people, prompting insights that will enable better tracking and
strategic decision-making. There is also hope that the data being gathered and
analyzed could help evaluate potential treatments.

New
initiatives explore how to utilize RWE against COVID

In the last few months, a rapid succession of programs and collaborations have launched with an aim to use real-world evidence and data to take on COVID-19 from various angles. Here is an overview of a few particularly notable initiatives:

1. Evidence accelerator

The Reagan-Udall Foundation and Friends of Cancer Research joined forces to create the COVID-19 Evidence Accelerator, a portal and process bringing many teams together to capture real-world data related to COVID-19 to address important questions.

2. Government / industry partnership

One of the collaborations that has already grown out of the COVID-19 Evidence Accelerator is between the FDA and New York-based, data science-driven tech company Aetion. Together, their goal is to “identify and analyze fit-for-purpose data sources to characterize COVID-19 patient populations and their medication use, identify risk factors for COVID-19-related complications, and contribute to the scientific evaluation of potential interventions.” Various real-world data sources and Aetion’s own proprietary analytic platform are key to the endeavor.

3. COVID-19 study-a-thon

In late March 2020, the Observational Health Data Sciences and Informatics (OHDSI) community held a four-day virtual study-a-thon to help aid decision-making during the pandemic. The study-a-thon had data scientists and researchers from around the globe designing and executing observational studies using real-world data from sources such as EHRs and administrative data. More events like this could be a great way of bringing the international community together to use RWE in the fight against COVID-19.

4. A data collection tool

Veracuity LLC, a biopharmaceutical safety informatics and analytics company, has designed an online survey tool that can be easily used by healthcare workers to collect and analyze real-world data on predisposing conditions, treatment strategies, outcomes and side effects that are experienced by COVID-19 patients. The tool, which is being distributed by the Alliance for Clinical Research Excellence and Safety, is expected to help fill in knowledge gaps about COVID-19, from risk factors to treatment outcomes.

5. An electronic registry

Health tech firm xCures has established BEAT19, an initiative created to gather knowledge about COVID-19. Essentially it is a real-time research study in which people – both COVID-19 patients and people who are well – are invited to share their symptoms, stress level, etc. The aim is to amass data that will enhance understanding of the disease in an empirical way.

To learn more about real-world evidence, read our previous blog posts on why pharma is interested in RWE and RWD, the benefits of RWE in drug development and the regulatory response to RWE.

How Are Regulatory Agencies Reacting to the Use of Real-World Evidence?

As we have discussed here previously, real-world data (RWD) and real-world evidence (RWE) offer many potential benefits in every stage of the drug discovery and development process, continuing on into post-market surveillance. With drug developers and other researchers becoming more interested in using RWD and the RWE that results from analyzing it, regulatory agencies have had to step up and work on producing guidance.

There are many
challenges that accompany RWD. Its various forms (e.g. EHRs, disease
registries, claims data) are not necessarily subject to the same
well-established regulations and protocols as clinical data. The data might be
inconsistent, unstructured, in multiple formats and it may not adhere to the
principles of FAIR data. As regulatory bodies consider RWE, they must think
about the quality of the data underpinning it.

The FDA offers
guidance

The Food and Drug Administration (FDA) took its first big step in December 2018 by publishing a framework for their real-world evidence program, which helped to lay out some of their goals and issues of importance to be addressed, such as how RWE will be used for regulatory decision-making for drugs, considerations for observational study designs and clinical trial design, data standards for submissions, regulatory issues around the use of electronic source data and more. Actual draft guidance for submitting documents using RWD and RWE for drugs and biologics then followed in May 2019.

The EMA grapples with real-world
challenges

Meanwhile in Europe, the European Medicines Agency (EMA) has also had to address the intense interest in RWD and RWE, though there are clearly concerns about whether real-world evidence can be credible evidence. In an article published in the journal Clinical Pharmacology & Therapeutics in October 2019, the EMA officials who authored it noted concerns that “acceptance of non‐RCT methodologies is tantamount to lowering the quality of evidence because these methods are prone to a myriad of undetected or undetectable biases.”

They remain optimistic
about the future for RWE, but are adamant about the importance of testing and
validation. “The ultimate key to achieving credibility is to start with an open
but ‘agnostic’ mind‐set and submit novel
methods to a fair, transparent, and prospective validation exercise,” they wrote.

The pharma response

The FDA has invited comments on its draft guidance, and the pharmaceutical industry has obliged. As reported in Policy & Medicine, a number of suggestions have come in from major players. Gilead, for instance, has proposed expanding the submissions list so that supplemental new drug applications and supplemental biologics license applications are included. Gilead has also suggested lab data be considered a source of RWD, and Novartis has suggested pharmacy claims should be considered a source for RWE. 

What is quite clear is
that we are in the early stages of what will be a long process, as regulators
work to formulate policy and guidance for a type of data that they are still
trying to fully define. Real-world data and real-world evidence have much to
offer in drug development and post-market, and it will be important to have the
guidance and cooperation of our most influential regulatory bodies.

In our next piece on
RWE, we will discuss the role of real-world evidence in the fight against
COVID-19, including a new research project spearheaded by the FDA.