Data mapping is the process of identifying and linking data from different sources in a clinical trial. This process is critical for ensuring that the data collected in a study is accurate, consistent, and reliable. In a clinical trial, data may be collected from a variety of sources, including electronic medical records, case report forms, laboratory results, and patient-reported outcomes. Data mapping involves identifying the different types of data that will be collected in the study, determining how they will be collected, and creating a plan for linking the data together. One of the key benefits of data mapping is that it helps to ensure data consistency across the study. By mapping out the data sources and creating a plan for how they will be linked, researchers can ensure that the data is accurate and consistent across all study participants and sites. Data mapping is also important for ensuring that data can be analyzed and reported in a meaningful way. By linking the different types of data together, researchers can create a comprehensive dataset that can be analyzed using statistical methods to answer research questions and generate insights

1. Welcome
Data Mapping in Clinical Trials
Abdulkadar Shakil Hakim
Bachelor Of Pharmacy
0008/012023
5/5/2023
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Sr. No. Content Slide number
1. Data Mapping in Clinical Trials 3
2. Data Mapping from eCRF to EDC 5
INDEX

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Data mapping in clinical trials refers
to the process of organizing and
linking data collected from various
sources during a clinical study.
The purpose of data mapping is to
ensure the consistent and accurate
transfer of information between
different systems used in a clinical
trial, such as electronic case report
forms (eCRFs), clinical trial
management systems (CTMS), and
data management systems.
What is Data Mapping in
Clinical Trials?

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Objective of
Data Mapping
in Clinical Trials
• Ensure data consistency: Data mapping helps ensure that the data
collected from different sources is consistent and that there is no
ambiguity in the definitions of the data elements.
• Improve data quality: By mapping data elements to a common
standard, data quality is improved, reducing the risk of errors and
inconsistencies in the data.
• Facilitate data integration: Data mapping makes it easier to integrate
data from different sources and to combine it into a single, cohesive
dataset for analysis.
• Enhance data security: By mapping data to a common standard, it is
easier to ensure that sensitive data is handled securely and in accordance
with regulations and industry standards.
• Streamline data analysis: Data mapping can streamline the data analysis
process, making it easier to perform complex statistical analysis and to
draw accurate conclusions from the data.

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Data Mapping from
eCRF to EDC

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eCRF are considered if studies are large with similar designs.
eCRF is less time consuming which makes it easy to the sponsor to
conduct large multi-centric trials at the same time.
Linking the data between two related pages of CRFs becomes easy and
quick, which leads to fast data cleaning, and regulatory submission.
Features eCRF (Electronic CRF)

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Electronic Data Capture (EDC) is a term used to describe
the use of electronic systems to collect and manage
research data in clinical trials.
EDC systems are used to replace traditional paper-based
methods of data collection in clinical trials, and offer
many benefits, such as improved data quality, reduced
errors, and faster data entry.
Electronic Data
Capture

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An EDC system typically includes a database that is used to store data from
multiple sources, such as case report forms, electronic diaries, and lab results.
The database is typically accessible to authorized users via a secure web-based
interface, and can be used to collect, manage, and analyze data in real-time.
EDC systems often include features such as audit trails, data
validation rules, and data-locking mechanisms to ensure the
integrity and quality of the data collected.
The use of EDC systems in clinical trials has become increasingly common in
recent years, as the need for more efficient and cost-effective methods of data
collection has become increasingly pressing.
Features of EDC

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1) Identifying
the data
elements to
be mapped
2) Defining
the mapping
rules
Testing the
mapping
Implementing
the mapping
eCRF Data
Mapping
EDC
Steps

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• involves identifying
the relevant data
fields in the eCRF
and determining
which ones need to
be transferred to
the data capture
system.
Identifying the data
elements to be
mapped
• defining how the
data elements in
the eCRF will be
translated into the
format used by the
data capture
system
Defining the
mapping rules
• include manual
data validation and
automated testing
using test scripts.
Testing the mapping
• Mapping
implemented in
the data capture
system, making
the data available
for analysis and
reporting.
Implementing the
mapping
Process

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