SDTM Mapping & SDTM Conversion
Get on-demand SDTM datasets & make informed decisions, earlier
Make earlier safety & efficacy decisions with rapid trial insights
It takes a load of time and effort to convert source data into Study Data Tabulation Model, or SDTM format. Costly programming resources are typically needed to get the job done. And when it’s left until the end of a trial, you risk costly submission delays. But it doesn’t have to be that way…
Use published standards in Pinnacle 21 to easily create SDTM mapping specifications. Then our SDTM mapping tools help you match the variables in SDTM with your source dataset variables. And because these tools are embedded in our clinical MDR, SDTM mapping can be done alongside study design! All that’s left is to run an SDTM conversion with one button click – and you’ll have trial data as soon as the first patient has enrolled!
Happy waiting up to 12 weeks for SDTM datasets?
…How about having your SDTM from First Patient In!
Why use Pinnacle 21 to create SDTM datasets?
Easier SDTM mapping
Automatically generate SDTM mapping specifications from standards and study content in your clinical MDR. Our SDTM mapping engine helps you map variables in SDTM with relevant variables in source datasets – providing both human and machine-readable mappings with ease.
Run on-demand SDTM conversions
Because SDTM mapping is done at study design, conversions can be done with one button click – as soon as data is collected. Now you can run SDTM conversions on-demand, with no need for costly programming!
Easily design SDTM datasets
Make compliant SDTM datasets before you’ve even collected any raw data! Don’t risk delays at the end of a trial trying to retrospectively make datasets SDTM compliant! Our continuous validation checks ensure your datasets comply with CDISC Define-XML, CDASH, SEND, SDTM and ADaM standards.
Get a regulatory preview
Preview your SDTM datasets in PDF and HTML formats, so you know exactly what regulatory reviewers will see. Then make any edits until you’re satisfied with all deliverables.
Reuse SDTM across studies
With our clinical MDR as your foundation, you can standardize SDTM mappings and datasets for future reuse. Using pre-approved, validated content avoids having to manually create standards for every study – reducing the risk of errors and increasing data quality and consistency.
Built-in compliance
The P21 platform is driven by SDTM rules. Any errors or deviations from the relevant standard are captured and resolved to ensure compliance. As such you won’t need to be an expert in the different versions of the standards, or the rules that apply to each. And no need to keep on top of updates to standards either!
Make informed decisions with real-time SDTM compliant data
How you’re better off with Pinnacle 21
See how drugs are performing in real-time
With on-demand SDTM datasets at the click of a button, you have complete visibility of how a drug is performing – as soon as the first patient has enrolled. With this insight you’re able to make informed safety and efficacy decisions – which could save lives if warning signs are detected – or allow you to adapt a trial based on data trends, potentially saving huge amounts of time and money.
Faster, more cost effective study build
Once you’ve established a library of standardized SDTM mappings and datasets, you’ll mostly be reusing existing validated standards. Study build will be far quicker as a result, saving you time and costs on programming resources, which may be diverted elsewhere.
Increase chances of timely submission
By aligning with SDTM from study design and doing SDTM mappings upfront, regulatory compliance is maximised throughout the process. Not only is data quality and compliance maintained throughout, you’ll avoid submission delays at the end since your data will already be in the required regulatory formats.
Easily create SDTM datasets with Pinnacle 21
1.
Decide which SDTM domain each CRF maps to
2.
Identify the SDTM domain variable each CRF question maps to
3.
Create raw datasets based on CRFs
4.
Create mappings & map raw datasets to SDTM variables
5.
Add CRF page links to SDTM variables
6.
Automatically create SDTM datasets
Why align with SDTM from the start?
‘Designing with the end in mind’, or aligning data with SDTM from the start of a trial, is key to delivering compliant trials, on time. Retrospectively trying to make collected data align with SDTM takes a lot of time and manual effort at the end. Worst of all, you risk causing delays when it comes to submission.
It’s best to consider the SDTM structure when designing CRFs, so that you’re aligned with CDISC SDTM standards before data collection. You’ll save a lot of time and effort. And as a result, you’ll be able to submit your study more quickly, and without the stress of manually mapping data at the end.
Standardize ADaM datasets and get data faster
As with SDTM, you can create and standardize approved Analysis Data Model (ADaM) datasets and definitions in P21, before collecting any data. Common SDTM and ADaM variables can also be standardized and shared between datasets. Now you’ll have a library of CDISC-compliant ADaM standards to reuse. And that means you can quickly map to datasets, and build more consistent studies, faster!
Not only that, you’ll be able to see relationships between your source data and analysis results data. Full transparency of ADaM data, including data origins and content relationships, also helps regulators see the integrity of your study data. And the analysis data reviewer’s guide can be drafted from existing content in P21, in preparation for submission.
Why design with SDTM in mind?
Designing with the end in mind’, or aligning data with SDTM at the start of a trial, is key to delivering compliant trials, on time. Retrospectively trying to make collected data align with SDTM takes a lot of time and manual effort. Worst of all, you risk causing delays when it comes to submission.
It’s best to consider the SDTM structure when designing CRFs, so that you’re aligned with CDISC SDTM standards before data collection. You’ll save a lot of time and effort. As a result, you’ll be able to submit your study more quickly, and without the stress of manually mapping data at the end.
Pinnacle 21 and Formedix have joined forces!
Our unified platform will improve collaboration and data flow across key stakeholders, whilst enabling faster, higher quality trials, from design to submission.
Find out more about ryze > visit formedix.com
Find out more about P21 > visit pinnacle21.com
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SDTM FAQs
SDTM refers to the Study Data Tabulation Model. It’s the standardized format required in the pharmaceutical and clinical research industry to organize and present patient data from human clinical trials to regulatory authorities, like the Food and Drug Administration (FDA). In 2004, SDTM was declared as the required standard format for data submission in clinical research. Proving data in line with the SDTM structure ensures consistency and enables more efficient analysis and greater transparency in clinical research.
SDTM datasets, or Study Data Tabulation Model datasets, are tables of data that are produced in line with SDTM standards. SDTM is the required format for tabulation data submission to the FDA and PMDA. SDTM datasets contain information relating to clinical trial patients, including demographics, treatments, adverse reactions etc.
SDTM datasets (Study Data Tabulation Model datasets) and ADaM datasets (Analysis Data Model) are closely linked. SDTM is designed for data tabulation, meaning SDTM datasets are used to collect and map raw patient data. SDTM is the source of ADaM datasets, in other words, ADaM is built from CDISC SDTM. ADaM datasets are used for analyzing the data from clinical trials.
SDTM mapping specifications describe how raw data is to be converted. They’re used by biostats programmers to convert data to the SDTM format. The process of developing SDTM datasets starts with identifying the required SDTM domains from your CRFs and raw data. Next, map the raw dataset variables (with a non-CDISC structure) to the relevant CDISC SDTM domain variables. That way, you’ll know which raw dataset will provide the data for your SDTM domain. Finally, list all the variables against each SDTM domain and describe how they should be programmed.
