Case reporting forms (CRFs) are the tools used to gather data during clinical trial research. The CRFs are typically designed and developed to be able to capture the data from trial participants, which are relevant to the clinical trial being done. All the data gathered in CRFs would then be organized into tabulation models, which follow a certain framework.
But, then, you’ll have to annotate CRFs to your SDTM. CRF annotations refer to data sets being configured to match their output tabulation models, you can check . The process of making sure that the raw information in the data sets will match those contained in the SDTM is called the annotation SDTM.
Here are some of the advantages and disadvantages of using annotated CRF SDTM in clinical research trials:
Pros
- Yields Consistent Data
The advantage of using annotated CRF SDTM for clinical research is that it’ll help your trial yield consistent data. Using the CRF annotations will help you map out the source of the data from your forms to your tables and data files. The raw data that’s converted into SDTM will match with each other and stay accurate throughout your analysis. You have to make sure that you set things straight from the annotation to interpretation and analysis.
Keep in mind that you’re analyzing data sets from several different parties in a clinical trial. You should make sure you’re making the correct specifications at the onset.
Getting consistent data results from your clinical trial is important. This is important for analysis and interpretation. It’s also important because multiple stakeholders will be given access to check the consistency of the data.
- Helps Attain Higher Efficiency
Another advantage of using annotated CRF SDTM for clinical research is that it can help attain higher efficiency in various processes, stages, and aspects of your clinical trial. Some of the areas that could increase efficiency are:
- Dataset Conversion Process
Handling and converting data are two of the most critical aspects of a clinical trial. During the entire trial, the research implementation team will have to submit and resubmit their data and findings to the Food and Drugs Administration (FDA). They have to do this several times. They also have to make sure that their data are correct and submitted in a format that’s acceptable to the FDA.
Automation will enable the research implementation team to execute their data conversion process without having to do any complex computer and machine programming. This will shorten the trial process and allow pharmaceutical companies to take their products to the market in less time.
- CRF Development
In the past, CRFs were manually designed and developed. But, now, automation tools are used to design and develop CRFs. This is a crucial part of the clinical trial because CRFs now have a built-in feature for validating data. This helps make sure that the forms are filled out correctly. It also helps ensure compliance with regulatory measures and requirements.
Cons
- Requires Additional Documentation
The process of handling processing, and converting data often take up a lot of time. Delays typically happen when there are inconsistencies or gaps in the data sets and tables. This is one of the justifications for the development of annotated CRF SDTMs for clinical trials. These help reduce delays in data processing and interpretation.
Using annotated CRF SDTMs also has its disadvantages. It’d add another set or layer of documentation to the existing conventional CRF design and development models. It also means that the software applications should be able to support the formats in which the data sets will be stored, processed, and deployed. For instance, you’ll have to provide additional documentation when you submit in SDTM format. Some of these are needed to support your data sets in XML formats and README documentation.
- Additional Cost And Preparation Time
Another disadvantage of getting annotated CRF SDTMs for your clinical research is that this will impose additional cost, effort, and time on the part of the research implementation team. For one, the CRFs would have to be re-designed and developed to match the SDTM formats. This needs to be done so that there won’t be any issue when the SDTMs are being configured and when the data sets are being annotated to the SDTMs.
Aside from additional expenses, putting up and using annotated CRF SDTMs would also entail additional preparation time. Since the CRFs would still have to be annotated to the SDTM, the CRFs can’t be used until after this task. This will cause delays to the research project. Another factor that should be considered is that the SDTM standards are still continuously evolving.
Conclusion
Using CRF SDTMs in clinical trial research is an innovation that’s enabled numerous clinical trials to improve their outputs, productivity, and efficiency. CRF SDTMs are highly useful in automating your entire process for data enlisting, saving, processing, and storing data sets, as well as tabulation models. Using annotated CRFs will yield consistency in data and help attain higher efficiency. The disadvantage is that the research implementation team will have to add more documentation, as well as put in more time and effort.