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• Now that you have a copy of the SDTM documentation, let’s find out what it is…

• SDTM is very simply a standard way to organize and format the data you collect in
a study, so that it uses the same variables, data grouping rules, and other rules as
all other Implementers. This way your datasets will “look” the same as everyone
else’s, and your data will be usable in a data repository that is built on this
standard model.

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As mentioned in the previous chapter, the SDTM standard focuses on one step, data
tabulation <ANIM1>, in the clinical research process. There are CDISC standards that
deal with data collection, data analysis, among others.

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The value of SDTM is that Standard data organization makes it easier to streamline
processes in data collection, processing, analysis and reporting

It also supports data aggregation and warehousing, and creates the opportunity to
mine and reuse your data

CDISC standards are also deeply routed in the concept of FAIR data principles—which
are a set of guiding principles that strive to make scientific data findable, accessible,
interoperable and reusable.

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Also, standard organization and formatting of your data can make it easier to share
data and work with your research partners, such as CROs and vendors.
Standardized data helps you perform due diligence and other important data review
activities. The SDTM model also organizes data in a standardized structure.

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And, a standard organization of every Sponsor’s data has the potential to improve the
regulatory review and approval process

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The regulatory reviewers typically struggle with non-standard data, because:

Reviewers have to spend a lot of time at the beginning of each review just to
become familiar with the individual datasets

When the data are not standardized there is a need for more programming
support than what is usually available, and

Even with review tools or programmers to help, it is difficult, and often

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You are a reviewer who has just received this submission, and you want to find out
how many Females were exposed to the Investigational Product.

It’s important to note that when data is submitted, it is in a table, or tabulated, view.
This is where the SDTM acronym comes from—a standardization of the table view.

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When you start to review the data you realize that none of the datasets have the
same name, so you must search for all the files that have demographic information.

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Then when you find all of those demographic files you realize the column names are
not consistent across the files…, <sentence continued on next slide>

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and even the way the data are presented is also not consistent. In some studies the
Females are represented with “F” and others with “Female”. Still others have
numeric codes and you will need more information than what is in these files to be
able to know which code means “Female” in each dataset.

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One final problem you might encounter is that you have no way to know whether
some of the subjects were in more than one study, and you don’t want to count
anyone twice.

These datasets are not organized or formatted in a way that will help you easily
answer this one simple question “How many females were exposed to this IP”.

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CDISC thinks there is a better way. Instead of making everything more difficult
for everyone inside and outside of your organization, CDISC recommends that
you standardize the way you collect data, so that when you organize it into
review datasets, it is consistent from study to study.

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Standardizing data collection will make it easier to organize the data into
consistent datasets that use the same naming conventions, the same rules for
what belongs in a Demographics dataset, and controlled terminology that is
used to represent some concepts, such as “is this person Male or Female”.

There are other rules that help everyone understand the data. For example:
We implement a unique subject identifier, or U-SUBJ<pronounced as in word
“subject”>-I-D) in order to show that the value for U-SUBJ-I-D is the same in
two different datasets, or two different studies.” .

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Reviewers are becoming familiar with standardized datasets and variables so they can
spend less time finding the data they need at the beginning of each review

Standard data allows the regulators to create software tools to help them review the
data

Software tools will allow the reviewers to look at individual submissions in new ways

And, Standard data will allow reviewers to create a data repository that will support
mining aggregated data

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To find out more about which data standards are currently accepted by regulators,
navigate to their website here.

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