Bayes Pharma Clinical.ai Trial Simulator Checking service... No Optimal Design schedule imported

Clinical Trial Simulation Platform

Monte Carlo PTA, dose finding, continuous and binary power, Bayesian adaptive updates, response-adaptive randomization, group-sequential decision rules, estimand-aware simulation, project workspaces, design versioning, audit trail, PDF/Word reports, deployed APIs, and decision dashboards.

SimulationMonte Carlo PTA

DesignAdaptive Trial

StatisticsPower

RegulatoryEstimands

ProductVersioning

APIDeploy

Project Workspace

Project Name

Description

Owner / User

Active Project ID

Ready.

Projects

Import Optimal Design Schedule

Optimal Design supplies the protocol-ready PK sampling schedule. This simulator keeps its focus on virtual populations, treatment arms, dose regimens, endpoints, dropout, power, probability of success, and operational feasibility.

Optimal Design ID

Open Optimal Design

No sampling design imported yet.

Imported Protocol Context

The imported sampling schedule, sparse cohorts, protocol language, and operational notes will appear here.

PTA / Dose Finding Inputs

Open Program OS

Pre-fill Typical CL/V/omega from this program's saved PopPK evidence in Clinical Program OS.

Doses, mg

Single PTA Dose

Target PTA %

N virtual patients

Seed

Target min

Target max

Typical CL

Typical V

Omega CL

Omega V

Ready.

PTA / Dose Results

Continuous Endpoint Power

Sample sizes per arm

Effect size

Alpha

Ready.

Binary Endpoint Power

Control response rate

Treatment response rate

Ready.

Power Results

Bayesian Adaptive Design

Prior successes

Prior failures

Observed successes

Observed failures

Target response rate

Decision threshold

Adaptive randomization arms JSON

Ready.

Adaptive Design Results

Genotype-Aware Cohort Stratification

Estimate the expected pharmacogenomic phenotype split for a planned trial cohort, from Pharmacogenomics / Precision Dosing's CPIC-guideline gene-drug pairs. Protocol/enrollment planning, not per-patient dosing.

Gene-drug pair

Planned cohort size (total N)

Ancestry mix (%, need not sum to exactly 100 -- normalized automatically)

South Asian / Indian

East Asian

European

African

Global / unspecified

Ready.

Group Sequential + Estimand

Information fraction

Observed Z

N subjects per arm

True effect

Dropout control

Dropout treatment

Intercurrent event strategy

Ready.

Advanced Design View

Multi-Look Boundary Schedule (Pre-Specified)

Generates the full look-by-look efficacy boundary table before the trial starts -- O'Brien-Fleming via the closed-form z_k=z(alpha/2)/sqrt(t_k), or Pocock via Monte Carlo root-finding of a constant boundary controlling family-wise alpha. This is the real design-stage boundary schedule (distinct from the single already-observed-look decision above).

Overall alpha (2-sided)

Number of looks

Spending function

Information fractions (optional, comma-separated)

Ready.

Efficacy Boundary Plot

Run Design + Create Version

Design Name

User

Ready.

Versioned Design Summary

Design Versions

Active Design ID

Ready.

Design Registry

Generate Report

Report ID

Ready.

Report Outputs

Deploy Trial Simulation API

Deployment Name

Deployment ID

Simulation override JSON

Ready.

Deployed Simulation

Decision Dashboard

Ready.

Decision View

Run History

Ready.

Audit Trail

Ready.