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

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

Dynamic Data Monitoring

For Dynamic Trial Design to work, you need a Dynamic Data Monitoring (DDM) platform integrated with a full EDC/eClinical platform to collect, monitor and analyze data in real time 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

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 the complex statistical and mathematical computations and simulations.

DTD, DDM and eClinical with IWRS

Dramatically Improve the Drug Development Process

How DTD with DDM, EDC and IWRS Impact Drug Development

With the CIMS closed system, you can typically determine if your drug is working, or failing, in 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 

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
  • Implement corrections as the trial moves forward
dynamic data monitoring screenshot

This is the Dynamic Data Monitoring engine at work – the machine intelligence and AI.

See Dynamic Data Monitoring in action

books

The CIMS DDM is backed by published research.

modules

Our eClinical platform contains 8 modules

compare

Compare DTD to the current standards of traditional Group Sequential Design (GSD) and Adaptive Group Sequential Design (AGSD)

person

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

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