Randomization assigns participants to treatment arms by chance rather than by anyone's choice or preference. Its purpose is to reduce bias, so the groups you compare start out as similar as possible.

Because participants are distributed randomly, any difference between groups at the end can be attributed to the treatment rather than to differences that existed beforehand.

In practice it gives you three things:

In Datacapt this is configured in minutes, from a simple 1:1 assignment to a multi-arm or cross-over design, with no custom scripts.

Randomization: assigning participants to treatment arms or groups, to minimize bias and strengthen the study's validity.

Randomized participant: a participant who has been successfully assigned to a treatment arm.

Randomization ID: a unique, incremental identifier generated automatically after randomization, for example R-0001.

Randomization allocation: the act of assigning a participant to an arm. Before allocating, Datacapt checks that the participant is not excluded, has not already been randomized in that randomization, and has answered every stratification question. It does not check inclusion criteria: confirming eligibility remains the investigator's responsibility.

Treatment arms: the treatment or intervention groups participants can be assigned to.

Treatment arm weight or ratio: the intended proportion across groups, for example 1:1 or 2:1. In Datacapt each arm carries its own Weight, from 1 to 10, in the Treatment arms setting. Two arms with weight 1 give a 1:1 ratio. Weights 2 and 1 give 2:1.

Datacapt offers three ways to decide the next allocation. The rest of this section explains each; the table at the end compares them side by side.

Static randomization uses a pre-generated list of assignments, arranged in permuted blocks following your ratios. Each new participant takes the next available slot in that list. In the configuration form this method is labelled Block.

It gives predictable group sizes, and suits you when you want simple, reproducible, controlled allocation.

Block sizes: each block size must be a multiple of the sum of the arm weights. With two arms weighted 1 and 1, valid sizes are 2, 4, 6 or 8. With weights 2 and 1, valid sizes are 3, 6 or 9. You can enter several sizes separated by commas: Datacapt then picks one at random for each new block, which makes the sequence harder to guess.

Dynamic randomization uses no predefined list. Datacapt calculates each assignment in real time, with an algorithm that minimizes imbalance across the stratification factors you choose, meaning centers, questions, or both.

For each new participant the system simulates every possible allocation, computes an imbalance score per arm, and assigns the participant to the arm that best preserves balance, with a controlled degree of randomness set by a probability p. This is the biased-coin method.

p is a value between 0 and 1. At 1, the participant always goes to the arm that minimizes imbalance. Lower values add a share of pure chance: at 0.8, the best arm is chosen 80% of the time. The imbalance itself is measured with the distance method you choose: Range, Variance or Maximum.

Import randomization uses an external list prepared outside Datacapt. Once uploaded, Datacapt follows it exactly as provided, so you stay aligned with the sponsor's pre-approved plan.

This is the option to use when a sponsor or statistician has already produced a validated sequence, for example to reproduce a legacy study or to satisfy regulatory documentation requirements.

Randomization can be configured as Open/Single-Blind or Double-Blind, depending on who should see treatment information.

Open/Single-Blind: every user who can open the participant's eCRF or the Randomization list sees the assigned arm, immediately after randomization.

Double-Blind: arm details are hidden by default from everyone. Participants and investigators are both blinded. Who can see the arm then depends on where they look:

Emergency unblinding: a user with the Emergency Unblind permission can reveal the allocation of a single participant, from the Unblind option in that participant's eCRF. The action requires the user's password (or an SSO sign-in) and a reason. A banner then shows who performed the unblinding and when, in UTC, and the event is kept in the audit trail. From that moment, every user with the Emergency Unblind permission sees that participant's arm.

You can optionally stratify the randomization so that key factors stay balanced across arms. Two kinds of factors are available in Stratification settings:

A study can hold more than one randomization, each with its own name, method, arms, blinding and stratification. This is how you handle studies with several phases, for example a first randomization at inclusion and a second one for a maintenance phase.

When a study has several randomizations, the Randomize button in the eCRF asks which one to apply. The name is required even when you only have one.

Each randomization keeps two separate lists. While the study is in Draft, every randomization goes to the Dummy list, so you can test the whole setup freely. Once the study is Live, randomizations go to the Production list, and the dummy data stays apart. For imported randomizations, you upload one file for each list.

Randomization is governed by a few distinct permissions in the role settings. Give each role only what it needs, especially on a double-blind study.

The page is called Randomization and trial supply management because it also covers the investigational product. Three tools work together: Kit types define the kinds of kits used in the study, the Kits inventory holds the kits themselves with their number, location, batch and expiration date, and the Allocation configuration maps kit types to treatment arms.

An allocation can be split into periods, so that a participant receives a different kit type at later visits. This is how a cross-over design is set up. See Kit inventory management.

Configure Static Randomization

Configure Dynamic Randomization

Prepare your randomization import file