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Randomization Overview

Overview of the ClinicalDataS Randomization module — treatment allocation, RTSM/IWRS, and supply management for clinical trials.

Last updated: 04/14/2026

Overview

The Randomization module (also known as RTSM / IWRS) manages treatment group allocation and blinding for clinical trial subjects. It ensures subjects are assigned to treatment arms according to the study protocol, with full audit trail and double-blind capability.

Key capabilities:

  • Multiple randomization algorithms — Permuted Block, Stratified Permuted Block, Big Stick, and Minimization Design
  • Double-blind support — treatment assignments are hidden from site staff; only authorized roles (Sponsor, Study Admin) can view
  • Site-stratified allocation — balance treatment arms across multiple sites
  • Pre-randomization eligibility checks — enforce inclusion/exclusion criteria before a subject can be randomized
  • Automated email notifications — notify designated roles when a subject is randomized
  • Real-time list generation — allocation is determined at randomization time, eliminating pre-generated list management

Workflow

  1. Install the Randomization app on the study.
  2. Create Subject Group Classes with treatment arm codes (required before algorithm configuration).
  3. Configure the app: blinding method, algorithm, treatment arms, event, pre-randomization questions, and email notifications.
  4. Go Live — once in Production, subjects can be randomized at the designated event visit.

Documentation

  • Configuration — Step-by-step setup guide covering algorithm options, stratification, additional pre-randomization data, and email notifications

Algorithms

AlgorithmDescription
Permuted Block DesignSimple balanced blocks with randomized assignment order — best for single-site trials
Stratified Permuted Block DesignSeparate block lists per stratum — best for multi-site Phase II/III trials
Big Stick DesignAdaptive design with configurable Maximum Tolerated Imbalance — better concealment than fixed blocks
Minimization DesignCovariate-adaptive assignment minimizing imbalance across multiple factors simultaneously
RandomizationRTSMIWRS