Dynamic Data Monitoring
An AI-engineered statistical package for monitoring clinical trial progress
Cutting-Edge Technology for Drug Development
The CIMS Dynamic Data Monitoring engine utilizes machine learning and artificial intelligence (AI) technology in an integrated, closed system with EDC and IWRS.
With the CIMS DDM platform, you can dynamically monitor and optimize your clinical trials — overcoming the drawbacks of classical adaptive group sequential designs to monitor trials without an Independent Statistical Group (ISG) and/or Independent Data Monitoring Committee (IDMC).
As new data is accumulated, the system will automatically compute the score function for chosen endpoints, confidence intervals and conditional power; update stopping boundaries; and perform simulations to predict the trend of a clinical trial.
Study Results in Real-Time
The CIMS Dynamic Data Monitoring system includes:
- Electronic Data Capture (EDC) system for managing patient data for clinical trials
- Interactive Web Response System (IWRS) for managing treatment assignment
- DDM Engine for complex statistical and mathematical computations and simulations
The integration is essential to ensure that the use of treatment assignment for calculating efficacy score is within the closed system.
- Compute treatment effect (point estimate and 95% CI) as trial progresses
- Compute conditional power as trial progresses
- Compute adaptive stopping boundaries dynamically
- Perform sample size modifications
- Assess futility for early termination
Dynamically Monitoring a Promising Clinical Trial
The video displays the estimated efficacy over patient accrual (or information time) with 95% CI, Conditional Power as well as the O’Brien-Fleming boundary. This trial could be early stopped at about 75% patient accrual due to efficacy.
Dynamically Monitoring a Hopeless Clinical Trial
The video displays the estimated efficacy over patient accrual (or information time) with 95% CI as well as the O’Brien-Fleming boundary. This trial could be early stopped at about 25% patient accrual due to futility.
DDM Leading to Clinical Trial Success
The video displays a trial based on Adaptive Sequential Design with initial sample size of 100 per arm and interim looks at 30% and 75% of patient accrual. Sample size re-estimation was performed at 75% patient accrual. The re-estimated sample size was 227 per arm. Another two interim looks were planned at 120 and 180 patients. The trial crossed the updated boundary at 180 patients. This trial would have been short based on the initial design. The trial eventually became successful with continuous monitoring and adaptation.
DDM Engine Use Cases
The CIMS Dynamic Data Monitoring engine can be also used for:
- Seamless phase 2/3 adaptive design
- Optimal dose selection
- Endpoint selection
- Population enrichment
- Real World Evidence (RWE) Monitoring
- Dynamic safety monitoring for pharmacovigilance
- Signal detection
- and more…
How to Implement
We offer variety of services and solutions for customers.
The Complete Suite
The suite consists of the CIMS EDC, IWRS and DDM engine. These three components are seamlessly integrated with internal I/O to each component.
Standalone DDM Engine
This is the Dynamic Data Monitoring engine at work – the machine intelligence and AI.
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