Whether you are planning for the data needs of your Safety Review Committee (SRC), Clinical Trial Steering Committee (TSC) Data Monitoring Committee (DMC/IDMC/DSMB), Clinical Trial Advisory Board or CRO Oversight, consistent clinical data monitoring and oversight is critical to a successful clinical trial. Regularly capturing and monitoring your trial's clinical data puts your teams in the position to better understand your trial's status and empowers your team with the information they need to make real-time changes as necessary to optimize your program.
This post breaks down five steps to defining a successful clinical data monitoring strategy. Following the steps below can save your programs time and money and reduce risk to patients and the program overall.
5 Steps To Defining A Successful Clinical Data Monitoring Strategy
Step 1: Identify Your Most Important Data Points
Clinical trial data can be overwhelming, and it is easy to lose focus on the more essential aspects of the study. A successful data monitoring initiative starts by defining which data points are most important to the analysis, goals, and organizational strategy.
Consider early the information reporting needs of your oversight teams, both formal and informal, and ensure that the data is timely, reliable, and structured for easy interpretation. Don’t forget to consider relevant data emerging outside of your trial. Data and processes around them spelled out in DMC/DSMB charters and SOPs should be factored into your workflow.
These data points can fall into several categories, including:
Patient Count
Patient Recruitment
Completed Trials
Participating Facilities
Step 2: Establish Data Context Format
Clinical trials increasingly generate more and more complex data. That means more reading and more digesting. This makes developing a way to get a sense of things at a glance more difficult if not more important than ever. The length and content of data requirements for your oversight teams will vary. Committees should define their data content and format needs upfront, bearing in mind the importance of time, trends, and comparisons in their analysis.
Collecting critical data is essential, but data points themselves aren't a reflection of success or progress. Comparison data points help to provide context to your raw data points.
By comparing your captured data points to the right metrics, your teams can better determine if the clinical trial is on the path to success or if additional measures are needed to ensure a positive outcome.
Standard benchmark metrics include:
Historical Data & Trends
Industry or other Comparative Benchmarks
Internal Goals & Targets
As data presentation is defined, consider the value of providing summary information from past data reviews, interim data, protocol, amendments, recommendations, and any complex medical or statistical issues that influence data interpretation.
Step 3: Implement and Test a Data Capturing Methodology
What good is identifying your critical data points if you're unable to track and capture this information? Before signing off a data monitoring strategy, walk through the collection process for each data point.
Take the time to identify potential roadblocks and bottlenecks. Are you relying on manual actions to collect your critical data? Or is the collection automated? Where possible, eliminate any manual collection.
This will help to ensure a smoother data collection process while also eliminating the potential for human errors.
Step 4: Educating Your Teams on How to Analyze the Data
Think through your monitoring and oversight teams and identify the individuals that would most benefit from the clinical trial data you're monitoring. Take the time to ensure that they understand how to interpret and analyze the data points they are looking at to support and improve their area of responsibility.
Don’t presume that all participants come to the table ready, willing, and able to understand both the data and the process that they will be engaged in. Committee members, including DMCs, often have a wide range of experience and training. Left unaddressed, training needs will impact the cost and success of your program.
Training programs do not need to be formal or time-consuming. They do have to be well suited to the team or committee itself, so be sure to determine your teams’ training needs upfront.
Critical Components of a Data Training Process Includes:
An understanding of why the specific data points have been chosen.
A walkthrough of how the data is collected.
Guidance on how the data points relate to established goals/benchmarks.
Recommendations on how to analyze the data.
Workflows on any actions needed based on the data.
Key elements of data security, including the importance of protecting unblinded data
Step 5: Select The Right System for Your Data Needs
The clinical trial oversight ecosystem is complex and highly regulated. Unfortunately, the work of most monitoring teams and committees is mired in 20th-century point solutions - creating data and process insecurity and variability.
These systems are often home-grown, dated & generic, disconnected, highly manual, and error prone. They increase costs, risks & delays at a time when we need trials that are fast, safe, and efficient.
Clinical trial oversight depends on accurate, up-to-date information that is securely and quickly available to decision-makers. Centralizing this information securely in the cloud means the right people will have instant, 24/7 access to the right information they need.
What’s more, rather than trial updates needing to be sent over insecure connections, the information can be uploaded or updated automatically, when it’s ready, and is immediately available. At the same time, the information is more private and protected than ever because it is only being shared within a single, secure platform that has high-level privacy protection.
Improve Your Clinical Trial Data Monitoring with Cloud Concinnity
Cloud Concinnity is an integrated platform that centralizes, simplifies, and automates control of data, communications, process management & reporting while unlocking the value of big data. Systems like Cloud Concinnity can make it easier for your team to review the data collaboratively while providing a centralized hub for data capturing processes, measurement strategies, and reporting.
Data collection, monitoring, and reporting are critical to a successful clinical trial. By taking the time to define your data collection intentions, you're more likely to experience long-term clinical success.