How To Manage Non-Standard Data In Clinical Trials

Clinical trials face many challenges due to a need for standardized data models. Usually, there are large datasets and variables to be collected that fall outside the defined standard models. This creates a gap in how clinical data is collected, managed, analyzed, and reported.

Additionally, miscommunication, ambiguity, and a lack of consistency may put the clinical trial and the organization at risk of failure.

Fortunately, the Clinical Data Interchange Standards Consortium (CDISC), the National Institutes of Health (NIH), the Food and Drug Administration (FDA), and other regulatory agencies have developed a set of standards that organizations can use to manage, analyze, and exchange non-standardized clinical data.

The CDISC provides organizations with an extensible framework to collect, analyze, and report data. Using these guidelines, Contract Research Organizations (CROs) and institutions can find ways to manage non-standard data. This article will look at different ways managing non-standard data can be achieved:


1. Use Supplemental Qualifier To Add Non-Standard Data   

A supplemental qualifier is a variable in a non-CDISC dataset that allows for adding non-standard, sponsor-defined variables to a Study Data Tabulation Model (SDTM). The SDTM has rules prohibiting new variables from being added to a data domain. However, a user may have additional data that cannot be entered into a domain during a clinical trial through the standard SDTM variable.

A supplemental qualifier dataset is introduced so the user can add the extra information in a ‘variable name – variable value’ format. Supplemental qualifier datasets always begin with “SUPP” followed by the SDTM domain it was created for. For example, if the dataset were created for domain exposure, the supplemental qualifier would be “SUPPEX.”

Organizations can use this method to quickly add non-standard data within the same dataset. It allows for better organization of the existing data and facilitates sharing with outside organizations, such as sponsors or CROs. You can check this guide to supplemental qualifiers to get an in-depth understanding of how they work.


2. Use CDASH To Guide Data Collection  

The CDISC Clinical Data Acquisition Standards Harmonization (CDASH) is a standard for collecting data in clinical trials. It’s a non-proprietary standard that helps sponsors and CROs to collect the same data elements during the trial.

CDASH aims to identify, define, and support the collection of commonly reported information across studies. CDASH provides a template of data items that should be collected in studies, which can help organizations manage non-standard data.

CDASH is an excellent way for teams to standardize the collection and management of clinical trial data, especially when multiple sponsors or CROs are involved.

It uses the “absence of evidence is not evidence of absence” rule, unlike in STDM, where lack of data means no record or nothing happened. Organizations can use CDASH to ensure that all participants collect the same data points and the data points are consistently documented.

Additionally, CDASH supports data traceability and quality. It allows a seamless data flow into the SDTM models through the CDASH-to-SDTM mappings. This is beneficial if you have data in non-standard data, as mapping first into CDASH mimics standard data collection practices. After mapping into CDASH, you can map the data into SDTM in a more standardized format.

How To Manage Non-Standard Data In Clinical Trials - News - Public News Time


3. Use Medical Coding Dictionaries To Standardize Languages  

Medical coding dictionaries are a great way to standardize non-standard languages. Dictionaries aid in the transformation of medical terms into standardized codes that can be used for data analysis and comparison.

They assign unique identifiers like Medical Dictionary for Regulatory Activities (MedDRA) or Logical Observation Identifiers Names and Codes (LOINC) to medical terms. This allows healthcare organizations to collect data and store it in a standardized way.

Each coding dictionary assigns and grades different events in a clinical trial. For example, MedDRA is used to classify adverse events, and WHODrug is used to classify concomitant medications. Organizations can standardize the language used in their clinical trials using coding dictionaries.

The dictionaries allow researchers to assess data across multiple studies, leading to a more accurate analysis of outcomes and results. It also saves time when analyzing data because researchers do not have to spend time transcribing and standardizing the non-standard language.

But to get the best out of the research, medical coding dictionaries should be used from the initial data collection stage to prevent non-standardized terms, incorrect abbreviations, and spelling mistakes.


4. Use Automation To Streamline Data Entry  

Manually entering large amounts of non-standard data can be time-consuming and require much manual effort. Automating data entry is a great way to streamline clinical processes and save time.

Organizations can use automation technologies like artificial intelligence, natural language processing, or robotic process automation (RPA) to reduce manual efforts in data entry. For example, RPA can automate transcribing non-standard language into a standard format or entering the data into a database.

Artificial intelligence and NLP can detect patterns in unstructured data and convert them into a structured format. Automating these processes can help organizations save time, reduce errors, and ensure data is standardized across different studies.


Non-standard data can be challenging to manage and standardize in clinical trials. But by using tools like CDASH, medical coding dictionaries, and automation technologies, organizations can streamline the process of managing non-standard data. This will help improve the quality of data collected and facilitate better outcomes analysis.

Ultimately, organizations should prioritize using these tools to manage all non-standard data accurately. This will help them maximize their research efforts and improve patient outcomes.

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