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Is Your Real World Data Credible? Part 2 in a series
How to identify the right set of data sources to address stakeholders' broad evidence needs
Nathalie Horowicz-Mehler, PhD, MPH, Senior Principal, Head of Real World Evidence Strategy, IQVIA
Tony Gottschalk, Senior Principal, Real World Solutions, IQVIA
Madhav Namjoshi, PhD, Senior Principal, Real World Solutions, IQVIA
Mar 08, 2021

In part one of this blog series, IQVIA experts detailed the various real world evidence credibility frameworks. The second and final blog in this series, will uncover how to identify the right data.

Know your stakeholders and their evidence needs

A successful real world evidence (RWE) strategy should credibly answer the questions that will inform the decision-making process. First and foremost, it is important to understand who the stakeholder is that real world data (RWD) are being generated for. As part of this, it is critical to answer the following:

  • What are the most pressing clinical or business issues for this stakeholder?
  • What is the market context?
  • What evidence best addresses these issues?
  • How can the evidence strategy evolve to account for a dynamic market landscape?

Clearly articulating the stakeholder’s current and long-term RWE needs will help determine the type of evidence that will inform decision making.

Identify your real world data sources

Many clinical and business questions can be answered by analyzing de-identified medical and pharmacy claims data. But depending on the research questions and patient population of interest, claims data are limited in the granularity and depth of clinical insights that can be generated. For these reasons, it is important to consider a broad range of data sources to address research questions and stakeholder needs (FIGURE 1).

Figure 1: The RWD value chain addresses evidence needs through a multi-sourced platform of healthcare data sources.

Additional RWD sources may include electronic medical records (EMRs), registries, labs and diagnostic data, genomic data, consumer data, patient reported outcomes (PRO) data from survey instruments, and actigraphy data from wearable devices.

Each of these data sources may provide information on different parameters of interest for the stakeholder. Some data sources may analyze as standalone data sets, and other data may be linked to provide richer context and longitudinal analysis of patients throughout their care journey, in different healthcare settings over time.

Multiple data sources are often required and integrated to address stakeholder needs

To address a broad set of research questions of interest around effectiveness, safety, health-related quality of life (HRQoL), and economic impact, a single RWD set is rarely sufficient. The true value of RWD comes from being able to connect disparate data sources, and in doing so, unlocking a more holistic picture of the patient journey. However, integrating these data sources presents several challenges.

  • Linkage: This is the connecting of appropriate patient records across disparate data sources while maintaining compliance with patient privacy regulation. In the United States, you may be able to link RWD sources into a unified data set, but General Data Protection Regulation (GDPR) considerations in the European Union may limit how the information can be used or accessed.
  • De-identification: In order to comply with patient privacy regulations (e.g. HIPAA, GDPR), privacy concerns are a key consideration, and tokenization may provide a way to gather data in a de-identified manner that is compliant with the Health Insurance Portability and Accountability Act (HIPAA).
  • Tokenization: This process transforms the information into a random string of characters that have no meaningful value apart from the mapping system. This allows for the transport of data in a format that protects patient privacy in case of a breach while permitting researchers to access the information.
  • Data harmonization/standardization: Alignment of data structures, terms, definitions, and code lists across disparate data sources may necessitate transformation and standardization of data. Common data models exist that can be used to streamline feasibility and statistical analyses across multiple disparate data sets.

Summary

In the wake of the COVID-19 pandemic, having an effective RWE strategy has taken on a new urgency. Carefully curated RWE can empower stakeholders, leveraging existing or bespoke RWD to enrich submission packages or drive corporate decisions. Data sources should be robust enough to enable accurate selection of a target patient population in sufficient quantities to meet sample size needs, capturing data in an accurate and timely manner, that is acceptable to regulators and payers. The linchpin for successfully executing RWD analysis is to clearly define the research questions and then evaluate different data sources based on their ability to provide credible answers.

A comprehensive, thoughtful analysis of RWD sources will ensure that stakeholder needs are exceeded.

If you missed the first post in this real world blog series that focuses on credibility frameworks, you can read it here.

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