Real-World Evidence in the Pharmaceutical Landscape

By Sunil Dravida, Abhishek Dabral and Jugal Sharma

In Real-World Evidence in the Pharmaceutical Landscape, life science industry experts Sunil Dravida and his co-authors have developed the first comprehensive overview of its kind on Real-World Data (RWD) in the pharmaceutical industry. The authors examine the challenges and opportunities in applying real-world data along the pharmaceutical continuum, from clinical development to medical affairs, health economics and outcomes, and marketing. They address the difficulties identifying the suitable data sources, ensuring compliance with privacy, security and regulatory requirements, and the big job of translating data into Real-World Evidence (RWE) to generate meaningful insights that can improve decision making by stakeholders and measurable outcomes that can enhance people’s health and well-being. This book is a must-read for those in the pharmaceutical industry involved with RWD, which includes just about every role, as healthcare is now dominated by the need for high-quality data that can enable better decision-making. This book is especially critical for those designing and leading RWD Centers of Excellence in pharmaceutical companies and the service providers supporting the RWD ecosystem.

• Language: English
• Publisher: Gatekeeper Press
• Release date: Dec 14, 2021
• ISBN: 9781662914096

Book preview

Preface

Regulatory agencies and payers have traditionally used Real-World Evidence (RWE) to monitor long-term safety and rare adverse events of marketed drug products. However, it was not until recently that the stakeholders in the healthcare industry began realizing and adopting RWE as a critical differentiator for value-based and outcomes strategies. Additionally, because of the tremendous innovations in digital technologies, such as cloud and advanced analytics, including artificial intelligence (AI) and machine learning, Real-World Data (RWD) collected from disparate sources in clinical settings have become more ubiquitous, linkable, and easily accessible for advanced insights. The latest laws, regulations, and growing pressure for pharmaceutical companies to demonstrate the value of their products to healthcare stakeholders have given drug manufacturers added incentives and imperatives to establish comprehensive RWE strategies and capabilities. The recent paradigm shift toward more personalized healthcare and value-based coverage and payment policies has funneled expanded usage of RWD to derive and leverage RWE.

Real-World Data (RWD) is generated from settings other than traditional randomized clinical trials and can be collected from a variety of sources, such as electronic health records, claims data, product and disease registries, patient-reported outcomes, and patient-generated data, including in-home use settings or wearable devices, biomarker and genomics data, lab data and social media. RWE is based on insights generated using RWD, and, as evidenced by many successful applications, RWE has proven to be transformative in all aspects of drug research, drug development (R&D), and commercialization.

In early drug discovery, RWE can help researchers understand patient profiles, disease burden and prevalence, and the effectiveness of the standard of care. Coupled with genomic data, RWE may create an opportunity to uncover biomarkers leading to more targeted drug development strategies for clinical development. RWD and RWE can improve trial design operation, including study feasibility, site selection, and patient recruitment. More importantly, RWD can be used to guide innovative trial design such as single-arm trials augmented with a synthetic control arm derived from RWD or pragmatic trials to generate evidence of comparative effectiveness. RWE that goes beyond the outcomes from the traditional randomized controlled trials plays a critical role in regulatory assessments for label expansion, coverage and payer decision, optimization of drug pricing, and drug supply chain and inventory management.

RWE has disrupted the way new medicines are developed. However, to realize the full potential and deliver the promise of RWE, it is imperative to have not only quality RWD but also the ability to extract insights from complex and heterogeneous data sets. Equally important is the clarity of regulatory policies regarding the use of RWE for registration purposes. In the published literature, a wide range of applications of RWE and use cases have been discussed, and opportunities and challenges expounded. Despite these publications, there is no single book systematically covering the latest developments in the field.

A significant barrier to the adoption of RWE is a ‘fixed mindset’ taught in medical school that evidence from the standard RCTs is amongst the most reliable sources of evidence. There are what can be called ‘evidence hierarchies’ that place conventional randomized controlled trials at the top, and d-rank RWE, by putting RWE at level 2 or even lower. Clinical researchers and providers are safe with RCTs, and they understand both the benefits and limitations. However, to truly make a change, these hierarchies need to be reexamined. Several years ago, the methods and tools for producing RWE did not exist, but now they are available, and there is a better awareness of what can be derived from the real world. The hierarchies of evidence should be revised accordingly.

This book discusses the latest advances in RWD and RWE for application in the entire pharmaceutical landscape. The contributors to this book are experienced pharmaceutical practitioners, providing a broad array of RWE perspectives, opportunities, challenges, and solutions.

Chapter 1 is an introductory chapter that covers some of the concepts of the next generation of RWE, including advanced analytics, AI, machine learning, and proven frameworks for successful RWE execution for pharmaceutical companies.

Chapter 2 covers the strategies of implementing RWE, including common data models for RWD.

Chapter 3 gives perspectives on generating RWD and analyzing for RWE along with some case studies.

Chapter 4 describes some of the challenges with the adoption of RWE.

Chapter 5 discusses the evolving role of RWE in the drug development process. Drawing from the published literature and use cases, it highlights the potential opportunities and challenges of RWE applications in transforming drug R&D and commercialization.

Chapter 6 gets into the details of applying RWE for market access and value demonstration.

Chapter 7 provides an overview of the Real-World Data elements for eligibility assessment, treatment exposure, and outcomes.

Chapter 8 covers the concepts of blinding and generation of RWE.

Chapter 9 discusses the intricacies of non-randomized observational studies.

Chapter 10 does a deep dive into how RWE can be leveraged to develop creative and innovative clinical trial designs.

Chapter 11 is about the nuances of value-based agreements and how to negotiate with payers.

Chapter 12 ties it all together by explaining the role of RWE in improving healthcare.

Chapter 13 captures practical perspectives from leaders in the pharmaceutical industry on RWE strategies.

We are deeply grateful to Jennifer Duff - Senior Partner, IBM Watson Health Consulting, Dr. Miruna Sasu - Chief Strategy Officer, COTA Healthcare, Ram Subramanian - Global Pricing Analytics Leader, Johnson & Johnson, Kerrie Holley – Director, Healthcare and Life Sciences, Google Cloud, Binbing Yu - Associate Director, AstraZeneca and Jatin Mehta, CEO, Metasense and McKinsey Panel member for their expert review and editorial assistance. We are humbled and very thankful to Lorraine Marchand, GM, IBM Watson Health for writing the foreword. Lastly, the views expressed in this book are not necessarily the views of the authors’ respective companies.

CHAPTER 1

Next Generation RWE

Most leading pharmaceutical companies are using Real-World Data (RWD) to generate Real-World Evidence (RWE) by applying advanced analytics. This enables them to deliver impact at scale. The key is to see how these pioneers can keep innovating and continue to be trailblazers.

An in-depth discussion on real-world data and real-world evidence is done in Chapter 5. For now, suffice to say that pharma companies collect data (RWD) from sources other than traditional Randomized Controlled Trials (RCTs) to measure and quantify the value of a medicine or therapy as the healthcare industry continues to focus on outcomes. RWE, which is a product of analyzing and deriving key conclusions from real-world data, has been utilized for decades. However, developments in technology, such as advanced analytics, have recently gained increased prominence and application. We can now better understand how patient features and behaviors influence health outcomes, which can aid in forecasting disease progression, a patient’s response to a treatment or therapy, and the likelihood of adverse events – this has a substantial impact on increasing the efficiency of R&D expenditure and shortening time to market. However, building the correct architecture and skills is critical to the success of an advanced RWE analytics deployment.

The Importance of Real-World Evidence

Advances in science, changing regulatory guidelines, complexity, and interoperability of data and cost and competitive pressures drive the healthcare system to place a greater emphasis on patient outcomes and value generation. As a result, 1) Payers are shifting to value-based agreements and contracts, 2) Providers are vying to get elite status with the payers, and 3) Patients are taking more responsibility and ownership of their outcomes.

In this changing environment, real-world evidence is becoming increasingly essential to provide optimal and timely treatment to the right patient and to measure outcomes and demonstrate the value of various therapies.

Advanced RWE analytics over the last few years has turned real-world data into a precious resource for pharmaceutical companies. RWE analytics use predictive models and machine learning to extract deeper insights from large data sets. These capabilities help pharmaceutical companies understand why people respond differently to medications, to discover why a medication works and how well it works, to run scenarios to learn how various treatments will compare, and to produce testable hypotheses for a variety of treatment combinations, comparisons, and endpoints (Figure 1.1).

Comparing Traditional and Advanced RWE Analytics

In RWE analytics, descriptive analyses and established matching techniques, like propensity score matching, are used to describe real-world use and outcomes and compare drug effectiveness in clinical trials. This is especially useful for therapists who wish to learn more about the patients using their therapy; for instance, how well patients adhere to therapy, how many patients have switched from first-line to second-line therapy in a specific timeframe, and if a drug treatment results in better outcomes than another drug for patients of the same general demographic at the population level. These approaches are well-established, but they are challenging to apply in general because there are only a few patient variables, and non-matching patients are excluded.

Figure 1.1 (Source: https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/creating-value-from-next-generation-real-world-evidence)

Advanced RWE analytics uses large data sets with rich information on thousands of patient variables with the help of sophisticated data engineering approaches. Then, predictive models, machine learning, and unsupervised algorithms are applied to these data sets to extract more profound insights. The models can predict outcomes for a new patient with unique characteristics by learning relationships between thousands of patient variables and patient outcomes.

Advanced RWE analytics provides answers to questions, such as which patient subsegments respond best to therapy X, which patient characteristics predict a switch from drug X to drug Y, combinations of patient characteristics causing disease progression, and how the characteristics of an individual patient interact with one another. It can also tell us the indications in a biological pathway where a drug is most likely to be effective, the risk of a patient having an event within a specific timeframe of seeing their doctor, the change in patient outcomes when switching from a drug to another, and the change in the total cost of treatment.

Leading pharmaceuticals are already extracting tremendous value from advanced RWE techniques through a range of applications, including predicting outcomes in type 2 diabetes and predicting findings of an ongoing phase IV cardiovascular trial.

Adopting advanced RWE analytics across a company’s entire value chain could generate significant additional annual revenue for any top-20 pharmaceutical company that uses them over the next few years. The typical scope cost model offers the opportunity to save millions in development spending by using RWE studies in place of RCTs and synthetic trial arms. Although other benefits besides cost savings are expected, companies will be able to use advanced RWE analytics to identify new markets, speed up the time to market, improve formulary position, negotiate better terms with payers, and create more substantial evidence of value proposition and differentiation for in-market products. These approaches could potentially lead to considerable top-line value.

For RWE, these examples are only the tip of the iceberg. Some innovative methodologies, like time-series modeling and generative adversarial networks, will lead the way to answer new questions that can’t even be imagined today. The ability to describe machine learning algorithms so that human experts can understand them enables increased transparency and comprehension and helps to fuel wider adoption. Data from medical claims, sensors, wearables, patient-reported outcomes, genomics, and social media are all at a point where they can yield a lot of new knowledge.

With technology giants and payers causing significant market disruption, healthcare companies should also develop a strategy to deal with such disruption. In one of the most ambitious attempts yet by a tech company to revolutionize a significant US industry, Google has partnered with one of the country’s largest healthcare systems to collect and analyze data from millions of patients.

Today’s leading companies employ analytics to increase their aspirations for markets and brands beyond one-off use cases. Data pipelines, analytical assets, engagement platforms, and ecosystems are built to implement use cases across different groupings (Figure 1.2). Rather than build analytical models from scratch, this approach provides businesses with the opportunity to utilize multiple datasets to create models, which are then industrialized for use in a variety of environments. Within time, businesses can embed the models into digital tools that are easy to use for various stakeholders: internal (R&D, market access, medical science liaisons, and so on) and external (healthcare professionals, payers, patients, and others).

Advanced analytical models should be built into an enterprise-wide capability stack.

Figure 1.2 (Source: https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/creating-value-from-next-generation-real-world-evidence)

Implementing an RWE Center of Excellence

Identifying the most optimal data sources and the accurate derivation of meaningful insights while dealing with massive amounts of often disparate patient experiences daily can be a very complex exercise. Real-world data can be obtained from existing sources or registries, including commercial data sources, such as claims or electronic medical records. The numerous existing data sources can be regional or restricted to particular healthcare facilities (e.g., specific hospitals), nationally representative or even multinational. They differ in their content and the quality of the data, sample sizes covered, inclusion and exclusion criteria applied, and the settings of care that are being covered (hospital setting vs. outpatient setting). In addition, each data source is ruled by its own terms and conditions that define data access that needs to be considered when designing an RWE strategy, as these have significant implications on the implementation timelines.

Figure 1.3 summarizes the main criteria used to assess the availability and suitability of potential data sources.

Figure 1.3 (Source: https://www.evidera.com/wp-content/uploads/2016/05/Real-World-Data-Strategy-A-Roadmap-for-Success.pdf)

In addition to a systematic appraisal of potentially suitable data sources, a thorough delineation of real-world data gaps and potential biases should be undertaken. In the context of multinational evidence generation activities, inevitably, a mix of database analyses and data collection studies across countries or regions will be required to achieve a robust evidence base that has been adapted to the needs of each market.

A well-designed real-world data strategy can result in a multinational patient-level repository of real-world information from various sources, including new data collection efforts. However, the utility of these custom repositories is greatly enhanced when a common data model that serves to standardize data vocabularies and formats is implemented. Standardization is highly recommended because it allows for the pooling and rapid analyses of highly variable and disparate data, which inevitably result from programs of real-world research, as well as the following additional benefits:

• Improved efficiency, through reduced programming time,

• Increased transparency as a result of analytics democratization and the opportunity to share coding algorithms,

• Reproducibility of results across datasets, and

• Faster time to data by leveraging automated data analytics tools.

A comprehensive data strategy can provide a framework for the organization and prioritize data sources and study types optimally suited to address the research questions of interest and encourage various stakeholders within and across life science companies to plan for more excellent and effective use of real-world data.

Figure 1.4 enumerates most of the real-world data sources along with the use-cases for real-world evidence.

Figure 1.4 (Source: Galson and Simon 2016)

Most pharmaceutical companies are building an internal Real-world Data Center of Excellence team to fulfill data needs and create an RWD ecosystem supporting RWE and insights. The mission of the CoE is typically based on an internal digital health data lake framework to advance the competitive advantage while delivering value to the enterprise. The CoE has a framework of processes for identifying needs, evaluating assets, and acquiring compliant & appropriate RWD to ensure data remains a corporate asset. There is usually a comprehensive approach to surface RWD use cases and gaps and map to potential vendor candidates.

The key is to follow a consistent and coordinated process to rapidly acquire and deploy RWD from external sources (third parties, vendors, etc.) that align with the internal strategic priorities in a sustained and compliant way. There are a few points to keep in mind:

• Understanding of cross-functional RWD needs, use-cases, and available data sources.

• Scanning, landscaping, and discovering new, innovative RWD sources from industry.

• Acquiring RWD through a streamlined process by collaborating on legal, compliance, information management, strategic sourcing, procurement, etc.

• Strategically aligning and collaborating with data partners to build high-quality data sources.

• Mitigating compliance risk and enable enterprise-wide transparency to ensure strategic value.

Here’s a list of some RWD databases in various regions that house patient-level data in different therapeutic areas:

Table 1.1

The majority of the RWD is privately owned, while some are in the public domain. Khosla et al. (2018) discussed that access to RWD falls into three categories: commercial, research collaboration, and developmental collaboration. Commercial data access is a fee-based option acquired through a licensing agreement with healthcare informatics vendors. The CoE will tap into the databases of the providers to address their research questions. Some significant research organizations have their own database, which can be made accessible through research collaboration and agreement. Development data access focuses on developing one’s own RWD through working with subject matter experts.

Here’s a list of key data fields that can be useful for generating the evidence:

Table 1.2

Also, it is essential to partner with multiple internal groups to enable RWD for researchers and analysts to generate insights and evidence (RWE):

• Partner groups that could be supported are HEOR, drug development, biometrics and data sciences, medical, patient safety, clinical trial applications, observational research, data sciences, etc.

• Data source types include EMR, labs, claims, genomics, patient-reported outcomes (PRO), linked datasets, and any other patient data collected outside clinical trial settings.

Advanced RWE analytics capabilities are becoming more important to early adopters as they invest in building these capabilities. For a successful RWD/RWE Center of Excellence, eight dimensions are emerging as particularly important. Research has proven that companies do not need to excel at all eight but should focus on building a leading position in a few of them.

1) Vision and Strategy

To uncover new market opportunities and generate value, best-practice companies build bridges between the marketing and R&D departments by using business-back approaches to identify the best possible products and value chain elements and then specify what value they want to create and how, and when. Having defined this granular aspiration, they continue to be incredibly focused on the products and development programs they have targeted, with a particular eye toward R&D, regulatory, market access, and commercial activities, while also employing RWE in other areas of the organization like medical affairs.

2) Quick Wins Approach

Some companies have had trouble convincing the business about the value of advanced RWE analytics after building RWE analytics platforms, aggregating data sets, organizing their company, and outlining operating models and processes. Other companies have found better results by implementing a different strategy. A proof-of-concept use case that showcases RWE analytics in answering an urgent business question is implemented to galvanize their organization from the outset. Sponsored by senior executives, the projects take advantage of cutting-edge data and analytics and a diverse group of stakeholders and enhance company-wide capabilities and assets. Employing these strategies will drive customer demand for RWE analytics across the entire business, enabling RWE to take the business out of the lighthouse and systematically pursue use cases along the value chain.

Typically, the CoE receives and tracks vendors from many teams and scans/screens for innovative RWD opportunities, evaluating fit for the RWD ecosystem. Some of the steps in the framework are:

Identifying Requirements:

• Engage with stakeholders to understand RWD needs and disease priorities.

• Compile data needs into business use cases by leveraging the data lake framework for digital health.

• Translate data needs into data gaps and data attributes of interest.

Profiling:

• RWD source identified by the evidence generation team.

• Screening the data source and going through qualification and assessment.

• Execution of the non-disclosure agreement with the data provider.

• Maintain a list of the sources with use cases.

Evaluation of the Data and Compliances:

• Compliance (GDPR, HIPAA, CCPA, COI, Sunshine Act) assessment exercise.