Study Setup and Data Input

Overview

The Prognosis wizard guides you through 8 steps to fully configure your clinical trial forecast. Each step builds on the previous one, capturing all the information needed to generate accurate demand projections and supply recommendations.

Pro Tip: Your progress is automatically saved as you complete each step. You can exit the wizard at any time and return later to continue where you left off.

Step 1: Countries and Depots

Configure your trial's geographic footprint and distribution network. You will set up your central depot (the primary manufacturing or distribution hub), add the countries participating in your trial, optionally configure local depots for regional distribution, and define shipping lead times between locations. Each country also requires site-level settings such as the number of clinical sites and site lead times.

For detailed configuration instructions, see Countries & Depots.

Step 2: Recruitment

Define when and how patients will enroll in your trial. For each country, you specify the expected number of patients, the First Patient In (FPI) and Last Patient In (LPI) dates, and the enrollment distribution pattern (linear, bell curve, or custom). You can also configure a screen failure rate if your trial has a screening phase.

For detailed configuration instructions, see Recruitment Configuration.

Step 3: Kits

Define the investigational products and materials used in your trial. Each kit type includes a name, category (Active, Placebo, Comparator, etc.), quantity, shelf life, and optional cost information. Most trials require multiple kit types, and you can add as many as needed.

For detailed configuration instructions, see Trial Kits.

Step 4: Label Groups

Organize countries by their labeling and packaging requirements. Different countries often have different labeling needs (language, regulatory text), and label groups help you plan packaging runs efficiently and track inventory by label variant. Each country must be assigned to exactly one label group.

For detailed configuration instructions, see Label Groups.

Step 5: Treatment Arms

Define your trial's treatment protocols, visit schedules, and kit dispensing rules. Each treatment arm has a name, allocation percentage (for randomization), a visit schedule (consistent or custom intervals), and kit assignments per visit. You can also configure drop-out rates at each visit to account for patient discontinuation.

For detailed configuration instructions, see Treatment Arms.

Step 6: Cohorts

Manage distinct patient groups with different enrollment windows. Cohorts are useful for dose escalation phases, staggered enrollment, or adaptive designs. Each cohort specifies a treatment arm, enrollment dates, patient targets, and participating countries. You can also enable titration rules to model patients moving between dose levels.

For detailed configuration instructions, see Cohorts & Titration.

Step 7: Production

Configure manufacturing parameters and supply constraints. This includes production lead time, safety stock and overage percentages (global, per-kit, or per-country), site seed amounts, and optional pre-planned lot configurations for existing inventory or confirmed production schedules.

For detailed configuration instructions, see Production Constraints.

Step 8: Actuals

Track real-world data for in-progress trials. Once your trial has begun enrolling patients or receiving kit inventory, you can enter actual enrollment numbers and current inventory levels. Prognosis uses this data to lock in confirmed numbers for past dates and recalculate future projections accordingly.

For detailed configuration instructions, see Actuals.

After the Wizard

Once you have completed all steps, you can:

• View Analytics -- Explore your demand forecast, production recommendations, and risk assessment
• Edit Configuration -- Return to any step to modify parameters
• Create Scenarios -- Clone your trial to test alternative assumptions
• Export Data -- Download forecasts for offline analysis or sharing

Continue to Demand Analytics to learn how to interpret your forecast results.