Data as of Oct 13, 2026A topic in Clinical Trial Management Software.
Reviewed by Dimitry Apollonsky
AI summary
SAS Clinical Enrollment Simulation holds a wide lead for modeling patient recruitment scenarios and site performance variations. However, Berry Consultants is the usual answer when buyers specifically evaluate trial criteria variations alongside complex simulations.
discrete event simulation modeling site performance and patient recruitment variations
enrollment forecasting and trial simulation within clinical workflows
simulating complex adaptive trials with variations across trial criteria
clinical trial management and enrollment tracking across operational sites
clinical trial simulation and enrollment predictive analytics
Mentioned in · Jul 5 – Oct 13, 2026
Google AI ModeOct 13, 2026
When optimizing clinical trial protocols—specifically testing how varying inclusion/exclusion criteria impact target populations and simulating enrollment timelines —the "best" option depends on whether you need an operational feasibility forecaster, an AI-driven protocol designer, or an open-source statistical tool.
The top platforms and approaches categorized by their core strengths include the following:
To help narrow down the ideal choice, let me know:
ChatGPT SearchOct 13, 2026
ChatGPT SearchOct 4, 2026
We are looking for software to model patient enrollment scenarios and test variations in trial criteria. What do you recommend?
For clinical trials, the best option depends on whether you want to test variations in inclusion/exclusion criteria, estimate how many patients qualify, or simulate enrollment timelines under different scenarios. These are related but distinct tasks, and the strongest choice is a platform that supports both eligibility sensitivity analysis and enrollment forecasting.
1. QuantHealth — Best for comprehensive trial-design simulations
(Best overall fit)
Lets teams vary inclusion/exclusion criteria and other protocol parameters, then evaluate predicted enrollment, clinical outcomes, safety, and operational feasibility. It also models site-level and global enrollment over time.
Best for: pharma and biotech teams comparing multiple trial-design scenarios.
2. Frekil — Best for testing eligibility criteria
Models how changing age ranges, diagnostic requirements, lab thresholds, or comorbidity exclusions affects the eligible patient population using real-world patient records. It also supports geographic site selection.
Best for: finding out how relaxing or tightening criteria changes the available patient pool.
3. Keizer Technologies — Best for protocol authoring plus enrollment simulation
Combines structured eligibility-rule creation with Monte Carlo enrollment simulations, sensitivity analysis, and optimistic, median, and conservative timeline forecasts (P10/P50/P90).
Best for: teams that want to test criteria changes while developing the protocol itself.
4. Trialsim — Best for a low-cost simulation pilot
A beta simulator that models how recruitment strategies, site configurations, and protocol design affect enrollment timelines. It reports uncertainty ranges from Monte Carlo simulations and is free for noncommercial use.
Best for: exploratory modeling before committing to an enterprise platform.
| Your primary objective | My pick |
|---|---|
| Compare many criteria variations and their downstream effects | QuantHealth |
| Estimate how many patients qualify under different eligibility rules | Frekil |
| Simulate enrollment while authoring a protocol | Keizer Protocol AI |
| Experiment with enrollment assumptions on a limited budget | Trialsim |
These recommendations are based on published product capabilities, not an independent head-to-head validation. Before choosing, verify access to patient-level or real-world data, geographic and therapeutic-area coverage, model validation, and the ability to export scenario results.
Are you primarily trying to simulate:
How changes to inclusion/exclusion criteria affect the eligible patient count
How long enrollment will take under different site and recruitment assumptions
Both criteria variations and enrollment timelines
Statistical power, sample size, and probability of trial success
Where are you in the trial lifecycle?
Planning a new trial or protocol
Managing an ongoing trial with enrollment data
Researching or building a simulation model
[Refine recommendation]
Yes. For modeling patient recruitment under different trial/eligibility criteria, the strongest fits I found are:
My shortlist: If your key question is “What happens to recruitment if I loosen/tighten criterion X?”, start with Prognica or Zlick. If you need statistically rigorous enrollment-risk forecasting, Cytel Enforesys is especially strong.
Google AI ModeOct 4, 2026
Modeling patient recruitment and accrual scenarios with varying trial criteria requires specialized clinical trial simulation and forecasting software rather than standard project management or static spreadsheets . These platforms use techniques like discrete-event simulation or machine learning to account for real-world variables (site startup delays, strict inclusion/exclusion criteria, screen failure rates, and geographic spread).[](https://condorsoftware.com/blog/best-clinical-trial-accruals-software)[[2](https://www.sas.com/es_mx/software/clinical-enrollment-simulation.html)](https://www.sas.com/es_mx/software/clinical-enrollment-simulation.html)
The primary software options handling patient recruitment modeling and scenario testing include:
If you'd like to narrow this down, tell me:
