What randomization is
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:
Scientific validity: the groups are comparable, so the results mean something.
Fairness: every participant has an equal chance of any treatment.
Transparency: the process is predefined, documented and reproducible.
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.
Core concepts
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 RD-0001.
Randomization allocation: the act of assigning an eligible participant to an arm, once every inclusion criterion is confirmed.
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 you set this in the Treatment Arms setting, entering 1-1 for two equal groups.
The three methods
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, also called block randomization
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.
It gives predictable group sizes, and suits you when you want simple, reproducible, controlled allocation.
Dynamic randomization, minimization, Pocock-Simon
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.
Import randomization
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.
Method | Logic | Inputs |
Block (static list) | Pre-generated list in permuted blocks. The next subject takes the next slot. | Arms, ratio, block sizes. |
Dynamic (minimization, Pocock-Simon) | Allocation computed in real time to minimize imbalance between groups, using the distance method you choose. | Arms, ratio, distance method (Range, Variance or Max), probability p. |
Import | External list uploaded and used as it stands. | A predefined list file in CSV or XLSX format. |
Open label or double-blind
Randomization can be configured as Open Label, meaning single-blind, or Double-Blind, depending on who should see treatment information.
Open Label (single-blind): every user can see the arm assigned to each participant, immediately after randomization.
Double-Blind: arm details are hidden by default from everyone. Participants and investigators are both blinded. Only users with the Unblind access permission can see allocation details.
For emergencies, a separate Emergency unblinding permission reveals the allocation for a single participant. Either way the workflow stays fully auditable.
Scope and stratification
You can set the scope of randomization, and optionally stratify to keep key factors balanced.
Scope per center: each center gets its own list or its own algorithm instance, so balance is maintained locally. This is usually what you want on a multicentric study, where enrollment rates and participant profiles differ between sites.
Stratification by questions: balance allocations across up to 10 variables, taken from radio or dropdown items in the eCRF.
⚠️ Warning: once a variable is used for stratification, its answer options are locked in the eCRF builder. This keeps the eCRF consistent with the randomization configuration, but it also means you cannot add or reword an answer option on that question afterwards. Settle the answer list before you stratify on it.