Real-world evidence (RWE) studies are increasingly utilized to complement randomized controlled trials (RCTs) by providing insights into the safety and efficacy of treatments in broader patient populations. RWE is derived from various sources such as electronic health records and patient registries, and informs decisions throughout the product lifecycle, including regulatory approvals and cost analyses. However, the reliability of RWE depends on a rigorous data collection strategy that addresses issues of data quality, representation, and appropriateness for specific research questions.

1. Real-World Evidence Studies: Introduction, Purpose,
and Data Collection Strategy
Real-World Evidence Studies: Introduction, Purpose, and Data
Collection Strategy
The first image that comes to mind when one thinks of where safety and
efficacy data for a new treatment is generated is a randomized controlled
trial (RCT) at a central site. Although RCTs remain the gold standard for
evidence generation of new treatments, they are limited in terms of their
applicability to broader patient populations with different demographics
such as age, ethnicity, and comorbidities thus limiting their
generalizability. RCTs are carried out under strict conditions and dosing
schedules which are often not observed in the real world and are
conducted for limited time periods which are usually not sufficient to
capture adverse events, especially in the case of chronic diseases. This
has led to a shift in the thinking of sponsors, drug developers, payers,
and regulators to consider the use of real-world data and
real-world-evidence studies to inform decisions related to the product as
well as to support reimbursement decisions.

2. Real-world evidence (RWE) is the clinical evidence regarding the usage
and potential benefits or risks of a medicinal product obtained from
real-world data (RWD). RWD is regarded as observational data that is
collected outside of a traditional RCT (1). Although the terms RWD and
RWE are used interchangeably they are two distinct concepts. Not all
RWD translates into RWE. RWE is obtained by detailed analyses of data
from different types of trials such as pragmatic trials, observational
studies which can be prospective or retrospective, late-phase trials, or
hybrid trials which are designed to collect data from patients in a
real-world setting. The data that is collected can be in various forms such
as electronic health records (EHRS), claims and billing data, product and
disease registries, prescription data, data collected from routine hospital
and physician visits, patient-reported outcomes (PROs), and mobile and
wearable devices. Recently, data from biobanks and ‘-omics’ data is
becoming a valuable source of RWD. RWE studies are intended to
complement data generated from RCTs by providing a detailed view of
the actual use of the product and effectiveness and safety data that RCTs
are unable to capture.
There has been a recent upward trend in the number of RWE trials
conducted. In 2021, the Global Data Clinical Trials Database recorded
194 RWE trials and the Food and Drug Administration (FDA) published
90 examples of the use of RWE to support regulatory decisions (2). Since
2018, the FDA has released several pieces of guidance to support the
use of RWD and RWE for regulatory decision-making for drugs and
medical devices and the type of data to be submitted to support these
applications. The two main drivers for recent interest and uptake of RWE
studies are:
● Ability to gather large amounts of data using wearables,
biosensors, digital platforms, and mobile devices from patients in a
relatively simple and cost-effective manner that is
patient-accessible and can be obtained and continuously
monitored in a real-world setting.
● Improvement and sophisticated analytical capabilities allow for the
processing of large amounts of data using machine learning,
artificial intelligence (AI), and data analytics.

3. Although the use of RWE is compelling, it is important to understand
certain limitations that can compromise its use in making sound
healthcare and policy decisions. Since the data that is analyzed is often
reported by patients it may lack accuracy or completeness, and quality
which can result in misinterpretation of data. Thus, it is necessary to
design studies that adhere to certain practices related to quality that
ensure reliable results. Despite this RWE studies are gaining popularity at
all stages of the product lifecycle by providing data that RCTs are not able
to provide.
Purpose of RWE throughout the product lifecycle:
RWE evidence studies can be used at all stages of the product lifecycle
and can generate a plethora of evidence in favor of new therapies along
with providing information to support clinical guidelines, regulatory
decisions, and reimbursement decisions. The following figure (Figure 1)
shows the various stages of the product lifecycle and how RWE studies
can generate value and information across them. It is important to
develop a robust and reliable data collection method and involve various
business functions when developing an RWE study to maximize the
potential of this study to answer several questions reflecting the
real-world scenario.
Clinical research organizations (CROs) are increasingly relying on real-world
evidence (RWE) to inform decisions throughout the product lifecycle. RWE is a type
of evidence that comes from sources outside of a randomized controlled trial and
can include observational studies, patient registries, or data from claims and
electronic health records.
CROs use RWE to gain insights into the safety and effectiveness of drugs and
medical devices in real-world settings. This information can be used to inform
decisions about drug development, regulatory approval, pricing, reimbursement, and
post-marketing surveillance. By leveraging RWE throughout the product lifecycle,
CROs can ensure that their products are safe and effective for patients while also
providing cost savings for healthcare providers.

4. Figure 1. RWE studies throughout the product lifecycle
RWE can be used by all stakeholders such as researchers to provide
data on the long-term safety and efficacy of treatments, by regulators to
assess post-marketing safety events and decide on approval, by payers
to determine cost/value analysis, and by physicians to develop clinical
guidelines and suitable treatment plans.
Data collection strategy for RWE studies:
There are various sources of that can be collected prospectively or
retrospectively, and each serves a specific purpose to support either
clinical or regulatory decisions. Some examples of RWD are:
● Healthcare databases include electronic health records (EHR) and
electronic case report forms (eCRF) which provide information on
clinical outcomes and patient background and demographics.
These include results of clinical and laboratory tests for patients.
● Patient registries that provide information on patient cohorts
having similar characteristics such as conditions and treatments
(condition-specific or product-specific registries).

5. ● Data from patients include social media networks and
patient-reported outcome (PRO) data and patient-survey data.
● Health insurance databases provide information on patient billing,
prescribing patterns, and claims.
● Data from biobanks and ‘-omics’ data.
Real-world evidence (RWE) studies provide valuable insights into the safety,
effectiveness, and cost-effectiveness of medical products. To ensure the accuracy
and reliability of these studies, it is essential to have a well-defined data collection
strategy. One of the best ways to do this is to use safety database management
services. These services allow for secure storage and retrieval of patient data, which
can then be used for RWE studies. They also make it possible to track changes in
patient records over time, allowing for more accurate results in RWE studies. By
using safety database management services, companies can ensure that their RWE
studies are conducted with reliable data that is collected in a timely manner.
Data collection is the most critical step of any RWE study which feeds
into what can be harnessed to support various decisions relating to
treatment effectiveness and utilization. This is important as RWD is
collected from disparate sources which are heterogeneous in terms of
collection methods, language, and formats thus making it difficult to
collate into a uniform format. An ideal data collection strategy for RWE
studies will include the following (Figure 2):

6. Figure 2. Essential elements of a data collection strategy for RWE studies
● Purpose: the first step in any data collection plan involves a clear
statement of the research question whether the data is to be
collected prospectively or if retrospective data is to be used. It is
essential that the data address the question and is fit for purpose.
The research question must be formulated a priori before going
further to collect data as each data source provides specific
information. Various points such as cohort, intervention(s),
outcome(s), period of data collection, and covariates should be
included as part of the research question.
● Identification of data source: finding a suitable data source is
the heart of the data collection process. Different data sources are
used for different purposes, for example, PRO data or that from
clinicians can be used to inform long-term safety or effectiveness
outcomes whereas claims data can be used for HTA and
economic assessments. Certain patient-specific or
disease-specific registries are applicable only for certain
geographical locations making it important that the data source
correlates with the question that needs to be addressed.
● Data source evaluation: a systematic assessment of the data
source(s) chosen involves consideration of the following factors:
access and patient representation (country/region, exclusion

7. criteria), content (patient characteristics, co-morbidities, diagnostic
and prescription information, quality of life, hospital information),
and costs and timelines (3). A holistic evaluation of all these
factors translates into a robust data strategy.
● Quality assurance: the suitability and appropriateness for using
RWD to generate evidence to support a particular purpose rely not
only upon the type of data but also, its quality. Parameters such as
reliability, transparency, accuracy, timeliness, completeness, and
consistency of data must meet certain standards to assure the
credibility of RWE. The FDA has released guidance on checklists
for assessing RWD relating to EHRs and registries (4, 5).
RWE studies are extremely valuable to pharmaceutical companies for
demonstrating product value and signaling any safety concerns.
However, it is necessary that RWD be collected and used prudently to
ensure the reliability of RWE studies. Understanding the drawbacks of
RWE in comparison to RCTs such as low internal validity and
non-uniformity in data collection is necessary to appreciate the
importance of both these studies in drug development. Ongoing
advances in AI and data analytics and generation and accessibility to
genetic biobanks are expected to further increase the usefulness of RWE
studies in all phases of the product lifecycle.
References
1. Real-World Evidence | FDA
2. Trends in Real-world Evidence Using Real-world Data | SDLC
Partners
3. Real-World Data Strategy as a Roadmap for Success | Evidera
4. Real-World Data: Assessing Registries to Support Regulatory
Decision-Making for Drug and Biological Products Guidance for
Industry | FDA
5. Real-World Data: Assessing Electronic Health Records and
Medical Claims Data To Support Regulatory Decision-Making for
Drug and Biological Products | FDA