Introducing
Dynamic Trial Design & Dynamic Data Monitoring
A breakthrough approach for clinical trial design and management
Reshaping the Future of Clinical Trials
Dynamic Trial Design
Dynamic Trial Design (DTD) is a new method for designing and managing clinical trials. DTD expands on the FDA’s recent guidance on Adaptive Design for Clinical Trials to overcome the drawbacks of traditional Group Sequential Design and Adaptive Group Sequential Design.
Dynamic Data Monitoring
For Dynamic Trial Design to work, a Dynamic Data Monitoring (DDM) platform integrated with a full EDC/eClinical platform to collect, monitor and analyze data in real time is needed, which eliminates the need for lengthy Independent Data Monitoring Committees and Independent Statistical Groups. CIMS delivers a one-of-a-kind, patented DDM platform that utilizes machine learning and artificial intelligence to dynamically monitor and optimize your clinical trial.
eClinical
Our full eClinical platform provides the EDC and Interactive Web Response System (IWRS) to capture patient data and manage treatment assignment. Our EDC and IWRS are integrated with the CIMS DDM to create a closed system that runs complex statistical and mathematical computations and simulations.
DTD, DDM and eClinical with IWRS
Dramatically Improve the Drug Development Process
How Our Clinical Trial Package Impacts Drug Development
With the CIMS closed system, we can determine if your drug is working, or failing, in nearly half of the time of the current standard.
Trial optimization
Optimize your on-going trial to maximize its success
Early termination of “hopeless” trials
Receive alerts to conduct a formal futility analysis, enrich your population, or modify your sample size
Timely terminate a “hopeless” trial
Drug safety detection
Continue monitoring drug safety and signal detection
Dose selection
Optimize a phase 2/3 combination trial by identifying potential doses for phase 3
Population selection
Intelligently identify the most effective subpopulation
Apply to an RCT or RWE setting for personalized medicine
Apply to an RCT or RWE setting for personalized medicine
Sample size re-estimation
Intelligently estimate an optimal sample size to maximize the probability of success
Model checking and correction
Check and verify the assumptions set prior to the initiation of the trial
Check and verify the assumptions set prior to the initiation of the trial
This is the Dynamic Data Monitoring engine at work – the machine intelligence and AI.
See Dynamic Data Monitoring in action
The CIMS DDM is backed by published research.
Our eClinical platform contains 8 modules
Compare DTD to the current standards of traditional Group Sequential Design (GSD) and Adaptive Group Sequential Design (AGSD)
The CIMS DTD and DDM were developed by industry pioneers
“We’re implementing Dynamic Trial Design in our upcoming study and expect it to reduce our trial costs by a significant amount.”
— Bob Smith, VP of Clinical Development, DS Biopharma