Clinical Trial Data Management: Explained
Clinical data management (CDM) is one of the most integral processes in any clinical study.
Data is at the heart of research and clinical trial management. Without proper management, the research simply cannot provide the necessary data for successful drug evaluation.
Clinical data management (CDM) therefore ensures that the research gathers a sufficient level of conclusive, quality clinical data for this purpose. This is achieved through a variety of techniques across several data collection and management areas, including case report forms (CRF), Data Management Plans (DMPs), and tailored clinical database designs.
As a professional field, clinical data management has gained significant traction in recent decades – mainly due to the increased need for faster delivery times, and advancements in data technology. Its importance to the clinical trial process cannot be understated, making it an area that every clinical research professional should understand.
We cover everything you need to know about clinical data management in this guide, from why it’s important, to how clinical data management works.
What is clinical data management (CDM)?
Clinical data management (CDM) is the process of collecting, cleaning, analysing and managing study data in clinical research. Clinical data is managed in line with internal protocols and regulatory standards in order to ensure compliant, complete and accurate data.
The main goal of clinical data management is to gather as much quality study data as possible. Clinical data is integral to the study, forming the basis of clinical analysis. This data lets researchers evaluate the treatment and understand how it works in human patients.
For example in drug development, clinical data is used to evaluate the safety and efficacy of medicine and determine its approval for market use. In order for this to happen, there needs to be enough data to make evaluations. What’s more, the data needs to be high-quality in order to draw reliable conclusions.
As a result, proper clinical trial data management is integral to the drug evaluation process.
CDM applies across all three main stages of clinical trials, and even occurs in pre-clinical phases.
In the pursuit to speed up the drug development process, clinical data management has become particularly important for achieving faster development times. With better data quality comes better reliability. This lets evaluators make quicker decisions with improved efficiency.
Why is clinical data management important?
Data is at the heart of clinical studies, and without it, there would be no way to draw conclusions or evaluate results. This puts clinical data management at the forefront of clinical research, and is a key consideration from pre-clinical research stages and throughout.
Clinical data management is important for four main reasons. CDM ensures:
- Quality data collection
- Compliance with regulatory standards
- Data security
- Efficient clinical research processes
Quality data collection
In the field of clinical data, data quality refers to how well the data can perform for its intended purpose. Data needs to be accurate, complete and consistent in order to be considered high quality – which is where clinical trial data management is essential.
CDM ensures that data is collected, stored and managed appropriately in order to avoid errors, inconsistencies and missing data. There are three main pillars of data quality that clinical data management processes aim to achieve:
- Accuracy – data that is true to life and error free.
- Completeness – datasets that contain the full-extent of the required data.
- Consistency – data entries that follow consistent formatting throughout.
Data collection can occur under any circumstance. Quality data collection can only take place under rigorous conditions, which is why appropriate CDM measures are essential.
It is vital to manage clinical data in the right way, ensuring the study can rely on high-quality, statistically sound data. This is integral to the study’s success.
For instance, a trial investigating a new drug would need data to prove that the drug is effective in achieving the desired effect. The research would also need sufficient safety data to prove that the drug is suitable for market use. If the data is flawed or insufficient, it cannot prove this.
Clinical studies must adhere to regulatory standards that, if not complied with, can invalidate the data. It is important for clinical data management teams to ensure the trial is carried out within the guidelines of official regulations and standards.
The Clinical Data Interchange Standards Consortium (CDISC) outlines a set of standards used for collecting and managing clinical data. These include:
- The Study Data Tabulation Model Implementation Guide for Human Clinical Trials (SDTMIG). This ensures that data is compliant for use by the Food and Drug Administration (FDA)
- The Clinical Data Acquisition Standards Harmonisation (CDASH). This ensures a standardised format for data collection throughout clinical studies, meaning data can be tracked and reviewed more efficiently across studies.
It is also important for clinical data collection processes to comply with internal study protocols. This ensures that the data entries are labelled consistently, formatted appropriately, and stored securely.
Clinical trials collect Protected Health Information (PHI) from patients and participants – one of the most sensitive types of personal data.
This makes data security and patient confidentiality a key consideration in clinical data management.
There are several measures that CDM teams use in order to protect PHI. This includes:
- Coded ID for laboratory specimens, administrative forms, reports, and processes.
- Study ID numbers for patient names are used wherever possible. The list that links patient names with the respective study ID is kept securely and should only be accessible to the research team.
- Password protected clinical databases and access systems.
Whilst everybody involved at the research organisation is responsible for protecting sensitive data, clinical data management teams have an explicit responsibility to integrate data security and protective measures into the study protocol.
At site level, clinical research associates (CRAs) are responsible for monitoring clinical activities, documentation, data systems and research procedures in line with data security and trial regulations.
Clinical trial data management ensures that the necessary data security measures are in place to protect sensitive health information. Additionally, data protection laws such as the General Data Protection Regulation (GDPR) and the UK Data Protection Act 2018 (DPA18) apply to clinical trials.
Thus, not only is data security important in CDM for protecting patients, but also to remain compliant with data protection laws.
Improved efficiency and organisation
Clinical data management processes also help improve trial efficiency. Effective CDM can result in a significant reduction in drug development time, meaning new treatments go to market much quicker.
This has become especially important in recent decades, where pharmaceutical industries and regulatory bodies demand more time and cost-effective clinical trials.
With structured, clean and organised clinical data, sponsors and regulators can make faster evaluations and decisions about the drug, determining clear and measurable actions. This also improves internal processes, helping researchers share knowledge and information more efficiently and keep everyone involved in the trial informed.
Clinical data management also helps organise internal processes. CDM teams manage, monitor and improve data quality assurance standards across the study, helping researchers follow standardised data collection and storage procedures.
The clinical data management process
Clinical data management begins at the earliest stages of research. Whilst it might seem as though data management could only occur once data has been collected, the process in fact begins even before the study protocol is complete.
The clinical data management process occurs in stages.
1. Case report form (CRF) design
Firstly, the CDM team develops a Case Report Form (CRF). This is used to define the specific data fields that the study will measure. This outlines:
- The type of data that will be collected
- The units of measurement that will be used
- Where the data will be stored
For example, the CRF outlines how adverse effects are to be measured in pharmacovigilance, as well as medical histories, follow-up visits, and status evaluations.
The case report form is referenced throughout the study, and ensures that all data handling procedures are standardised across different teams.
2. Data management plan (DMP)
Secondly, a data management plan (DMP) is put together. This is a guide that describes the planned clinical data collection, processing and management activities throughout the research.
This is similar to the case report form, which outlines what kinds of data collected. A DMP is slightly different, in that it outlines how the data will be collected, managed and handled throughout the study.
A DMP is also very similar to a study protocol, which describes the planned activities of clinical research, although a DMP relates explicitly to clinical data.
3. Database design: clinical data management systems (CDMS)
Next, clinical data managers develop databases that are tailored to the needs of CDM tasks.
Using the fields outlined in the case report form, and the practices outlined in the data management plan, the database designer forms a data management system that complies with these protocols.
The database is designed to capture the specific data measures outlined in the CRF, and will usually only validate data that follows these data entry requirements. This is intended to minimise the possibility of human error during the data entry process, ensuring that invalid entries queried and corrected within the system.
The database is then stored in a clinical data management system (CDMS), a dedicated data capturing system that is specifically designed to hold clinical data. This is tested prior to the trial to ensure it works as intended and the system validates data entries appropriately.
4. Data mapping and validation
In order for data to be processed, it must be collected in a valid format. Data validation ensures that entries comply with the correct standards, and that the information can be used reliably during drug evaluation.
For example, date and time entries must follow a standardised format in order to be valid. Date, month and year data can be formatted in several ways, mainly DD/MM/YYYY, or MM/DD/YYYY. The same format should be used consistently across all data entries, each containing valid inputs.
Date formats are a good example of data validation issues. In the MM/DD/YYYY format, any month of the year (MM) that exceeds the number 12 would be considered invalid – since the months of the year do not exceed 12. However, if this initial value is mistaken for the day of the month (DD), the value may be inputted as any number up to 31, making it invalid.
The clinical data management system is designed to detect inconsistencies and assess data entry. Clinical data managers also perform testing to screen for data quality issues throughout the process.
5. Data conduct
In the data conduct phase, the data is reviewed for quality assurance. This assesses the quality of the data, and how well the CDM process has adhered to the protocols outlined in the case report form (CRF).
This process also reviews the data for any discrepancies, checking the accuracy of data entry and compliance with the correct medical coding.
The main goal of data conduct checks is to assure data quality throughout the clinical research process.
6. Study close out
The study close out occurs once all data has been collected and patients have stopped receiving treatments. During this final stage, the clinical data management team follows several steps to comply with study close out regulations and ensure the final dataset is complete and secure.
Firstly, CDM teams confirm that all the collected data is in the clinical data management system database. They then check that all data queries have been addressed and resolved. Next, the data undergoes cleaning and quality control checks – this identifies any data errors, duplication, validation, anonymity issues and more.
Finally, the database is locked and secured. This ensures that the clinical data is protected and is not adjusted prior to drug evaluation.
Areas of responsibility in clinical data management
Clinical trials involve various types of data collection throughout the process. This can include broader data, such as demographic, primary endpoint and safety data. This also includes specific patient health data, such as health records and adverse event reports.
As a result, data activities can vary throughout the study, which is why there are several responsibilities associated with clinical data management. Usually, trials will appoint personnel or teams to specific CDM divisions, whereby they are responsible for a particular area of clinical data management.
These areas can include, but are not limited to:
- Case report form (CRF) design
- Data collection
- Data validation
- Database design
- Data privacy
- Data compliance
- Patient recorded data
- Quality control
In summary, clinical data management is a vital component of clinical research. Without CDM, the research cannot rely on accurate, quality data.
Clinical data is essential for the drug evaluation process. Where the data collection and management process can be optimised, this can help speed up the research and result in faster drug delivery times.
Additionally, CDM plays a central role in clinical data compliance, ensuring that the trial operates in line with regulatory standards and procedures.
Clinical trial management and CRO services
At Simbec-Orion, we support your development needs with a flexible and agile approach to clinical trial management. We offer a full range of additional CRO services from phase 1 to phase 3 to design and deliver clinical development projects that meet your specific requirements.
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