Our validation tools help you ‘spot the difference’ and correct any inaccuracies.
Is your data in good shape? Is it FDA compliant? Formedix carries out systematic checks throughout the design and build process to make sure of it.
One thing we do is compare what you’re supposed to have against the files you’ve got. This could mean comparing studies against specifications, or against your organizational standards. That way you get to see any differences before approving your study designs.
If you’re working with a CRO, you can check what you get back from them too. Formedix shows how your form designs will look in your EDC system. Like a preview. So you can compare this with the EDC datasets your CRO passes on, to see if they match up.
Formedix also validates your datasets against relevant industry standards – including SDTM, ADaM and SEND. Even if a CRO does it for you, we can still check that your datasets conform to the right standards.
Formedix makes sure of it, with built in rules to automatically validate against non-compliance.
Types of validation
For clinical trials, there are many types of validation to be done. For example:
There are many different steps to consider. Some of these can be included in the design process to build in conformance from the start. When it comes to validating CRFs with clinical oversight, it’s important to be able to quickly review and adjust things. Such as form layout, question wording and controlled terms. A web based system makes this easier, since teams often work in different locations.
Easier validation – work directly in your EDC
If you’re working directly on content in your EDC system – as you can do in Formedix – this makes validating CRF specifications against eCRFs in your EDC system much easier. It’s literally a case of, ‘what you see is what you get’ in the EDC. This is also true for datasets. By producing a specification for each stage of the EDC to SDTM conversion process, you can review and test the design with test data – before the EDC system is live. Once your clinical data is available, by complying with the expected dataset specifications, you know the conversion process will also work as expected.
Easier compliance with CDISC standards
For submission deliverables – such as SDTM datasets – the metadata and data within the datasets (and Define-XML) must conform to the CDISC SDTM-IG standards. In basic terms, this standard dictates the column names, labels and column order of the datasets. Compliance against this standard can be measured if the SDTM specification itself is compliant. Formedix cross checks the clinical datasets against the specification for you.
Formedix makes SDTM dataset creation far easier, with CDISC SDTM metadata templates. That way you build your datasets in line with CDISC SDTM rules, right from the off!
The pitfalls of programming validation
Programming validation requires a different approach. The most common cause of problems are human errors. After creating an SDTM compliant specification, this is then divided into separate programming tasks. If each task is then programmed by two different teams, a comparison of the outputs will eliminate most human errors. But you must still compare the output with the specification to ensure compliance.
Rule based templates make SDTM datasets easier
Each SDTM dataset starts as a specification in the Formedix platform. Our dataset design tools contain the CDISC SDTM metadata as templates. So Formedix builds your datasets using the CDISC SDTM rules. The templated metadata is then extended using the selected CDISC NCI Controlled terms, and any supplemental qualifiers, value level metadata etc. is added.
Built in SDTM compliance
The FDA and PMDA publish compliance rules for clinical data submissions, including SDTM. The output must ultimately be compared against the rules. And any deviations must be eliminated or fully documented. By building in e.g. SDTM compliance at the start of the process, you maximise compliance throughout the process. Key factors such as correct use of controlled terms must be built into the EDC CRF’s and SDTM conversion process. This includes EDC edit check programming and data management checks programmed to highlight data issues early.
Quicker eCRF design
With Formedix metadata management tools, you can quickly build and publish case report forms (eCRFs) for clinical review. As the visual elements (questions, radio buttons, controlled terms) are captured along with the EDC database build specification (variable names, question settings, edit checks etc), an auto-generated specification is used for both the eCRF review and the EDC deployment. So you don’t need to check the EDC CRF’s against the eCRF specifications. They’re loaded automatically. This also means that any changes can be reflected in the review/build eCRF immediately. There’s no delay. You don’t need to create word specifications, for example, or manually make screenshots in the EDC.
Each SDTM dataset starts as a specification in the Formedix platform. Our dataset design tools contain the CDISC SDTM metadata as templates. So Formedix builds your datasets using the CDISC SDTM rules. The templated metadata is then extended using the selected CDISC NCI controlled terms, and any supplemental qualifiers, value level metadata etc. is added.
This specification document forms both the guidance for programming teams, and also the Define-XML metadata for the dataset deliverable. The dataset specification is saved in Formedix, alongside the EDC build documents. This gives a central place for all your study metadata. Once the basic SDTM dataset structure is agreed, additional programmer instructions are added to each variable, domain etc. within the electronic specification. Any updates or clarifications are immediately captured in Formedix, and the software generated programmer specifications (e.g. excel sheets) are automatically updated. And the same metadata is used for the submission Define-XML, which is automatically generated.
The SDTM Define-XML is independent of the clinical datasets (and data programmer). Therefore it can be used as a benchmark to check the dataset content against, to ensure compliance with the CDISC SDTM standard.
Remember, if the Define-XML metadata is extracted from the clinical datasets, this is not suitable for use as a validation specification. Any errors in the datasets will just be recreated in the Define-XML, removing the ability to provide an independent quality check.
Formedix has been a valuable resource and their services have added value to our platform and data management capabilities. ...
How we help you manage study changes
If SDTM conversion is done before first patient enrolment, as a study progresses, inevitably there will be changes. And these changes will need to be made in the EDC system. This can be difficult. The programming team can’t easily track changes within the EDC system. Or easily quantify the impact of a change.
The Formedix platform stores the EDC design. So when an update is made, the SDTM programming team can clearly see any changes in Formedix – without even going into the EDC system. You can then compare the EDC clinical dataset specification against the updated versions to see the impact on SDTM programming. That means you can make adjustments during the eCRF update process – rather than after EDC deployment when data collection is already underway.
Detect and fix errors early on
As clinical data is entered, there will naturally be errors or incomplete information in the EDC. If SDTM programming is complete, periodic EDC data extracts can be converted into interim SDTM datasets, and SDTM validation can be done using the standard tools for FDA submission. These results can then be fed back to study data managers to correct errors early on. This typically reduces the burden of ‘data cleansing’ query resolution prior to database lock.
From this process, a ‘Reviewers Guide’ document can be created. This documents any deviations from the SDTM/FDA rules in detail as the data is collected. This ensures that all details are recorded for each issue. Check out the PhUSE reviewers guide template and examples.
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