🔬 Simulation

Models complex scenarios — patient risk trajectories, clinical trial sample size calculations, recruitment forecasting — giving decision-makers quantified confidence before committing to a course of action.

Agent-Bound Tool — Not available for direct subscription. Included automatically when you subscribe to: ClinTrialOps AI · ClinicalPath AI

Agent-bound tools work inside agent workflows — you never need to configure or call them separately. View all tools →

Tool Details

TypeAgent-Bound

Used byClinTrialOps AI · ClinicalPath AI

Data isolation✓ 6 layers

Audit trail✓ Immutable

This tool works inside agent workflows automatically. It is included in every result produced by the agents that use it.

Features

What Simulation does.

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Trial recruitment modelling

Simulates patient recruitment trajectories — modelling different recruitment assumptions to identify the most realistic timeline.

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Patient risk trajectory modelling

Models how a patient's condition is likely to evolve under different treatment approaches — providing clinical decision support based on quantified outcome probabilities.

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Sample size calculation

Calculates required sample sizes for clinical trials under different statistical assumptions — providing the power calculations that protocol design and regulatory submissions require.

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Scenario comparison

Compares multiple scenarios side by side — showing how outcomes change under different assumptions and which assumptions have the greatest impact.

Advantages

Why it matters.

Decisions made on quantified probabilities

Making important decisions on the basis of modelled outcome probabilities is substantially better than making them on intuition.

Trial design errors identified before commitment

Simulation identifies assumptions that are unlikely to achieve trial objectives — before the protocol is finalised and the trial begins.

Resource allocation based on evidence

Understanding which assumptions most affect trial outcomes allows sponsors to allocate resources to the activities that most reduce uncertainty.

Clinical risk assessment made quantitative

Translating clinical judgement into quantified risk probabilities improves both the quality and the documentation of clinical decisions.