There are many common questions people have when creating Define-XML metadata for their datasets. One of these revolves around how to handle data that can come from multiple sources, such as a CRF or eDT. In the simple case you just create a Value List, and specify the Origin on each Value instead of on the Variable.
Use of consistent terminology within and between studies is critical to enabling an efficient trial process. CDISC standards such as CDASH, SDTM, SEND and ADaM standardize the structures to use when collecting and submitting data, but what about the data values themselves?
This post gives an overview of CDISC's standardized controlled terminology and how it should be used to collect and submit data in a way that speeds trials and enables cross study analysis.
It's a safe bet that most people's first introduction to CDISC is through the SDTM. This is a content standard that ensures clinical data is submitted in a consistent manner, helping reduce review time and facilitating cross study analysis. Another content standard, ADaM, aims to perform a similar function for analysis datasets. Likewise SEND defines standardized domains for non-clinical data.
Adoption of these standards is driven by regulators such as FDA and PMDA, who mandate that data must be submitted in these formats.
This post gives a very brief overview of each model, how they fit in with the wider clinical trial process, and how you can get maximum benefit from them.
This latest post in our "Introduction to CDISC" series is all about Define-XML. It's known by many due to it being required in regulatory submissions, but why does it exist and what benefits can it bring you?
In the past sponsors submitting to FDA were required to submit a PDF describing their submission datasets. As we all know PDF is great for viewing on screen or printing, but the information inside it can't be interpreted by a computer in any meaningful way. Enter CDISC's Define-XML model...
Over the last few years our industry has become familiar with CDISC standards. This has largely been driven by regulation, with national regulators such as FDA and PMDA mandating the use of submission standards such as SDTM, SEND and ADaM.
This post shines a spotlight on the lesser known Operational Data Model (ODM) standard, which is often overlooked as it's not required by any regulators. Why should you be interested in it? Because it can be used as a standard way to define forms, independent of the data collection system. These forms can then be turned into organizational standards, driving data quality and consistency. Finally they can be used to drive your data collection system. In short, they put you in control.
With the right tools in place sponsors and CROs can define data collection standards while remaining flexible in their EDC choice. They can also take control of their EDC build to save time and money. Standardizing your data collection means greatly reduced future study build cost due to re-use and increased consistency between studies.
Let’s start at the start - what is CDISC? The Clinical Data Interchange Standards Consortium is an organization dedicated to helping improve medical research by driving interoperability through data standardization.
Formedix have been strong advocates for the use of CDISC data standards in clinical and non-clinical research for half my life now, ever since we realised how it could transform our business by enabling the rapid design and build of clinical trials. We quickly focused our company around use of CDISC standards, and are now industry leaders in CDISC software, professional services and training.
Over the next few weeks we’ll be publishing a series of blog posts giving an overview of the various CDISC models. Our aim is to help you understand how you can make the most of these industry standards. If we all work together using the same standards we can optimize our clinical trials by increasing data quality and reducing design and execution time. Ultimately that means getting more products to the market with less cost.
Standardized questionnaires often contain questions that should only be answered depending on the response to a previous question. Until now, such data has been submitted inconsistently to FDA, as CDISC SDTM does not provide any guidance on how to indicate that the questions have been "logically skipped". Thankfully FDA have just updated their Study Data Technical Conformance Guide to standardize how information about these logically skipped questions should be submitted. It's fairly simple...
With FDA now regulating for all new studies to use CDISC standards when submitting data, it's vital that our processes are up to date with the latest standards and fit for purpose.
An FDA presentation at the recent PhUSE Computational Science Symposium tried to shed some light on how we're doing as an industry. It analysed all eCTD submissions from January-February 2017 that used standardized data.
You might be surprised how the submissions fared...
I recently took to LinkedIn Pulse to share my thoughts on the challenges organizations face in getting new drugs to market. In this blog, I also explore the initiatives taking place to overcome these challenges and ultimately optimize the drug development process.
In our most recent learning webinar, CEO Mark and I looked at how you can optimize both new and legacy studies using CDISC standards. CDISC’s ultimate goal is ‘creating regulatory submissions that allow for flexibility in scientific content and are easily interpreted, understood and navigated by regulatory reviewers.’ and as proud CDISC advocates (for longer than we care to remember!), we’ve recognized the benefits of using the standards in your studies.