Overview

Big Stick Design (BSD) is an adaptive randomization procedure that allows treatment allocation to drift away from perfect balance up to a predefined Maximum Tolerated Imbalance (MTI). Once the imbalance reaches the MTI, the next subject is deterministically assigned to the under-represented arm. Otherwise, each assignment is made with equal probability (or a configurable soft-randomization probability).

BSD provides better allocation concealment than Permuted Block Design because assignments are less predictable — the "forced" balance point depends on the running imbalance rather than a fixed block boundary.

How it works

Let D = (number of subjects in Arm A) − (number of subjects in Arm B).

• If |D| < MTI → assign each arm with equal probability (or the configured soft-randomization probability).
• If D = +MTI → force assignment to Arm B (restore balance).
• If D = −MTI → force assignment to Arm A (restore balance).

Example — MTI = 3, 1:1 allocation:

Subject	Random draw	Assignment	Running D
1	0.71	TRT	+1
2	0.33	PBO	0
3	0.88	TRT	+1
4	0.62	TRT	+2
5	0.41	PBO	+1
6	0.91	TRT	+2
7	0.55	TRT	+3 — MTI reached
8	forced	PBO	+2

The imbalance never exceeds 3 at any point.

When to use

Use Big Stick Design when:

• You want stronger allocation concealment than Permuted Block provides.
• The trial is single-site and does not require stratification by subject characteristics.
• You are willing to accept slight final imbalance in exchange for better masking.

For multi-site trials or subject-level stratification, use Stratified Permuted Block Design instead.

Who uses this screen

The Randomization configuration screen is used by the Study Administrator to set up and manage the algorithm, study groups, eligibility questions, and notifications. Site staff (Investigators, CRCs) interact with randomization through the subject data capture workflow at the designated randomization event — they do not access this configuration page directly.

If you cannot open the Randomization app or the Edit buttons are greyed out, verify that the study is in Design status. Configuration changes are not permitted while the study is in Available, Locked, or Frozen status.

Typical workflow

1. Open the study and navigate to Randomization in the left sidebar.
2. Open the Blinding Method and Algorithm settings card and select Big Stick Design.
3. Save the algorithm selection — the Big Stick Design settings card appears.
4. Open Big Stick Design settings and configure study groups, MTI value, and imbalance rules.
5. Configure Select event to Randomize — choose which study event triggers randomization.
6. (Optional) Configure Additional data before Randomization — add eligibility questions.
7. Configure Email Notification — choose which roles receive alerts on randomization.

Step-by-step configuration

Step 1 — Open the Randomization app

Sign in to your ClinicalDataS instance, open the study, and select Randomization from the left sidebar under Installed Apps.

The Randomization page lists all configuration cards. If the study is in a non-Design status, most Edit buttons are disabled. To enable editing, change the study status to Design via the Change Status button on the study home page.

Step 2 — Select the Big Stick Design algorithm

Click Edit on the Blinding Method and Algorithm settings card.

Field	Options	Description
Blinding Method	Single Blind	CRC and study staff can see the subject's study group.
	Double Blind	Only Sponsor and Study Administrator know the subject group.
Randomization Algorithm	Permuted Block Design	Single-list randomization using balanced permuted blocks.
	Stratified Permuted Block Design	Separate lists per site/stratum combination.
	Big Stick Design	Allows imbalance up to a predefined MTI before forcing balance.
	Minimization Design	Covariate-adaptive allocation minimizing group imbalance.

Select Big Stick Design, then click Save.

The page reloads and the Big Stick Design settings card appears below Blinding Method and Algorithm settings.

Step 3 — Configure Big Stick Design settings

Click Edit on the Big Stick Design card.

Setting	Description
Type of imbalance evaluation	Count based — absolute difference in number of subjects per group. Ratio based — compares actual vs expected proportions. Cost based — assigns weight to treatment groups.
Study Groups	One row per treatment arm. Each row requires: Label (display name), Code (short identifier, e.g. TRT, PBO), Weight (allocation ratio weight), Description (optional). Use + Add more study group to add arms.
MTI Value	Maximum Tolerated Imbalance — the maximum allowed imbalance between arms. For count-based evaluation: 2–4 is typical for a 1:1 trial.
Imbalance level	By Site — evaluate imbalance separately for each site. By Study — evaluate imbalance across the entire study.
Imbalance scope	All active subjects — count all enrolled subjects. Exclude Withdrawn Subjects — exclude subjects who have withdrawn from the study.
Force assignment	Toggle — when enabled, forces assignment to the under-represented arm when the MTI is reached.
Soft Randomization Probability	Probability (0–100%) of assigning to the optimal (balancing) arm. Recommended 70–90% for large studies, 100% for small studies. Higher values improve balance but reduce unpredictability.

MTI guidance

Trial size	Typical MTI	Notes
< 50 subjects	2–3	Tight control; slight loss of concealment
50–200 subjects	3–5	Good balance between concealment and balance
> 200 subjects	5–8	Large trials tolerate larger imbalance without meaningful impact on power

Tip: Setting MTI too low (e.g., 1) approaches deterministic assignment and reduces allocation concealment. Setting it too high (e.g., equal to sample size) approaches simple randomization with no balance guarantee.

Click Submit to save the settings.

Step 4 — Select event to Randomize

Click Edit on the Select event to Randomize card.

Field	Description
Select Event	Choose the study event at which randomization takes place. Only non-repeat events are supported. Use Reload to refresh the list if you recently added events. Click Manage Events to navigate to the event setup page.
Randomization form name	Optional. Override the default name of the randomization CRF. Leave blank to use the system default.
Position of randomization form	Where the randomization form appears within the selected event. Default: At first position.

Click Submit to save.

Step 5 — Additional data before Randomization (optional)

Click Edit on the Additional data before Randomization card to define eligibility questions that site staff must answer before a subject can be randomized.

Three question types are supported:

Section	Behaviour	Example
Inclusion question	Must be answered Yes to proceed.	"NYHA Class II or III confirmed"
Exclusion question	Must be answered No to proceed.	"Active malignancy"
Open question	Free-text or other answer type; no pass/fail logic.	Any additional data collection

The Options toggle lets you replace the default Yes/No labels with custom text (e.g. Có/Không, Agree/Disagree) for studies in other languages.

Use + Add inclusion question / + Add exclusion question / + Add open questions to add rows. Click Submit to save.

Step 6 — Email Notification

Click Edit on the Email Notification card to configure which roles receive an automated email when a subject is randomized.

Field	Description
Send email notification to following recipients	Check one or more roles: Study Administrator, Study Sponsor, Site Monitor, Study Monitor, Investigator, Clinical Research Coordinator.
Also send email to following recipients	Enter individual email addresses (up to 30), separated by colons. Useful for external stakeholders not in the system.
Email Subject	Subject line of the notification email.
Email Content	Rich-text body of the notification email. Click View all Variables to insert dynamic placeholders (e.g. subject ID, randomization date).

Click Submit to save.

Comparison with Permuted Block

Property	Permuted Block	Big Stick Design
Allocation concealment	Weaker at block end	Stronger — harder to predict
Maximum imbalance	Block size / 2	MTI
Predictability	Higher (especially small blocks)	Lower
Simplicity	Simple	Slightly more complex
Pre-generatable	Yes	Not meaningfully

Regulatory acceptance

Big Stick Design is described in Soares & Wu (1983) and is recognized in the randomization literature, though less commonly used than Permuted Block. Document the MTI value and procedure in the randomization SOP and protocol. Discuss with your biostatistician before selecting BSD for a confirmatory trial.

Related topics

• Randomization Overview — all configuration cards and blinding options
• Permuted Block Design — simple balanced blocks, most widely used
• Stratified Permuted Block Design — adds site and covariate stratification
• Minimization Design — covariate-adaptive allocation