ryze BY SECTOR
- BOOK A DEMO
- FREE TRIAL
Map raw datasets to target datasets in ryze, then SDTM automation is just 1 click away.
As it stands, your source datasets (from your library or EDC) need a lot of work to get them into SDTM datasets. That takes time, labor, and programming skills = money.
ryze SDTM automation makes life easy. There’s no coding, and we use templates to speed things up. Our data mapping automation tools help you match the variables in SDTM with the relevant variables in your source datasets. Forget trying to work out what your source dataset headings will be. Once you’re done, press a button for your SDTM datasets and SDTM define.xml files.
Use our clinical trial automation software to make SDTM datasets upfront. You can even create them before you’ve collected any patient data. That way, you get SDTM conversion done early on, and can check that your datasets will meet regulatory requirements. You also get a head start on mapping your ADaM datasets back to SDTM.
Easily design datasets
Templates let you add domains and variables that are compliant with the relevant versions of CDISC standards. Tailor them to your needs by adding additional metadata – such as supplemental qualifiers, derivation methods and value list metadata. Our clinical metadata management tools help ensure that your datasets comply with CDISC Define-XML, CDASH, SEND, SDTM and ADaM standards.
Create standards for reuse
You can arrange your datasets into organizational standards for reuse in future. This increases data quality and saves so much time and resources.
Easily manage CRO specs
Use ryze to make specifications if you’ve got a CRO designing datasets for you. When you get datasets back, simply check them against your original specs to ensure they’re as you expected.
What will my datasets look like?
See how your SDTM datasets will look to regulators reviewing your submission. Either PDF or HTML format. This includes links to pages in external documents, like Annotated CRFs.
We are using the Formedix platform to design CDASH-compliant, example CRFs for the CDISC eCRF Portal. We can create, visualize, edit and approve eCRFs in the platform, then export them in ODM-XML to use in the CDISC Library and standards packages. Formedix is one of CDISC’s longest standing members. CDISC is keen to work with technology partners that help pharmaceutical organizations to adopt and keep pace with emerging CDISC standards.
Formedix has been a valuable resource and their services have added value to our platform and data management capabilities.
Your study can also manage other metadata in ryze, such as SDTM mappings. These describe how the original (or source) datasets from your EDC system map to the CDISC SDTM standard. Our CDISC SDTM mapping tools let you make and validate simple variable to variable mappings, as well as more complex mappings with many steps.
Mapping data upfront helps ensure that all the relevant data is collected. ryze clinical trial building and automation software shows what your EDC datasets will look like – before you get them. So you can do your mappings early on. Then crack on making your SDTM define.xml files once your first EDC datasets are ready.
And if you include SDTM mappings in your organizational standards, you can easily re-use them. That means faster study designs, and better quality data.
In ryze, you make human-readable mappings that clearly describe how datasets and variables in your original (source) datasets map to their equivalents in SDTM. From there you can create a metadata specification for programmers.
You can also create machine-readable mappings with our easy-to-use mapping language in ryze. No programming knowledge is needed. Once your mappings are done and approved, use them to turn your source datasets into SDTM datasets (using our templates). You’ll save time and money as there’s no need for programmers to double program data conversions from your specifications. Reports give you full traceability of all mappings to ensure data is accurately mapped.
SDTM conversion is when you turn your raw (source) datasets into SDTM datasets. This is done using the SDTM mappings and templates in ryze. You can do this at various stages, from study start to submission. SDTM dataset creation during the clinical trial lets you check your data integrity – as soon as data becomes available.
We can automatically upload your source data into ryze – whether it’s in SAS Transport v5 (.xpt), dataset-XML or CSV formats. Or you can manually upload data, for example, from legacy studies.
Data transformation is simple too. Your data is already mapped at study design. So you can run conversions automatically as soon as your trial data comes in. Not only that, you can automatically download the data in SAS Transport v5 (.xpt), dataset-XML and CSV formats. Our SDTM validation tools ensure that datasets conform to the relevant CDISC standards.
CDISC built SDTM to provide a standard way to organize and format data in order to streamline the processes of collection, management, analysis and reporting of clinical trial data.
The CDISC ADaM standard is designed to derive data and for analysis in clinical trials.
SDTM is built on observation classes, each one having associated domains. A domain is a group of observations that share a common topic such as vital signs, adverse events and demographics. Find out more on all you need to know about SDTM.
SDTM conversion is when you turn your original (source) datasets into SDTM datasets.
SDTM is designed for data tabulation and ADaM is designed to derive data and for analysis. SDTM is the source of ADaM data. SDTM collects and maps raw data while ADaM is all about creating data that’s ready for analysis.
Our platform is built on new and previous versions of CDISC standards and templates. Select the version of the standard you want and the platform guides you through. Compliance and validation are built in so you cant go wrong!
Our SDTM mapping tool lets you map your source datasets from your EDC system to SDTM datasets and from ADaM datasets back to SDTM easily. Plus you can see what your EDC datasets will look like early on so you can define mappings earlier. You can create metadata specifications for programmers. And there’s no programming required to do your mappings. Once they’re done, you can standardize them and use them over and over again. You’ll save time and money and you can run a report to ensure mappings are accurate.