Common Randomisation Methodologies Implemented in IRT
By Kevin Venner, Jennifer Ross
What is Randomization in a Clinical Trial?
Randomization is the process for how subjects are assigned to Treatment (groups, arms, etc.) in a clinical trial with introducing a deliberate element of chance. If randomization is not utilized or utilized inappropriately, then assumptions may be made on upcoming Treatment assignments. Knowledge of what Treatment is being assigned next can consciously or unconsciously influence decisions on whether to enroll a subject or which subject to enroll next. This influence is called Selection Bias. When properly implemented, Randomization protects against Selection Bias to ensure the observed Treatment effect is due to the Treatment itself and not due this bias.
Further, randomization helps achieve the required number of subjects per Treatment, which is also known Treatment Balance. With the random allocation of subjects throughout the trial, it is expected that each Treatment will have similar subjects for evaluation.
Interactive Response Technology (IRT) enables the global execution of randomization and medication management across multiple sites. This removes the need for site-specific code-envelopes and other burdensome manual randomization processes. Instead of relying on an unblinded contact to track randomization, the IRT maintains an auditable dataset including details of each subject’s randomization transaction.
Determining how to design randomization in the IRT begins by reviewing the clinical trial’s protocol. Typically, the protocol provides randomization details such as the Treatments, allocation ratio, number of subjects enrolled (sample size) and other information as applicable (e.g., stratification factors, cohorts, etc.).
This article focuses on the most common randomization methodologies implemented in IRT:
- Central Randomization
- Stratified Randomization with Blocks Pre-Allocated to Strata
- Stratified Randomization with Blocks Allocated On-Demand to Sites
How is Central Randomization Methodology Implemented in IRT?
Central Randomization is when all subjects are randomized within the same scheme regardless of any subject characteristics or demographics. Clinical trials use a Central Randomization design when Treatment safety and efficacy is evaluatedacross all randomized subjects, and no sub-group analysis is planned. This evaluation requires Study-Level Treatment balance.
For an example, assume a protocol’s randomization design specifies:
- Treatments: 2 (Active vs. Placebo)
- Treatment Allocation Ratio: 1:1
- Sample Size: 20
- Stratification: None – N/A
To obtain the Study-Level Treatment balance given the sample size (N=20) and ratio (1:1), the IRT will need to randomly allocate 10 subjects to Active and 10 subjects to Placebo. This is achieved via a Blocked Randomization List, sometimes referred as Randomization Schedule. The Randomization List is generated with a specified Block Size that includes randomly ordered Treatment assignments within each block. For example, if the Block Size is 4, then for every 4 records, 2 Active records and 2 Placebo records would appear in random order. This blocking technique is the basis for each type of Randomization List utilized across the 3 common Randomization Methodologies. See below for an illustration of a Blocked Randomization List for Central Randomization.
Example 1: Central Randomization List
| Sequence Number | Randomization Number | Treatment Code | Treatment Description | Block Number | SubjectID |
| 10001 | 10001 | A | Active | 1001 | 1 |
| 10002 | 10002 | A | Active | 1001 | 2 |
| 10003 | 10003 | B | Placebo | 1001 | 3 |
| 10004 | 10004 | B | Placebo | 1001 | |
| 10005 | 10005 | A | Active | 1002 | |
| 10006 | 10006 | B | Placebo | 1002 | |
| 10007 | 10007 | A | Active | 1002 | |
| 10008 | 10008 | B | Placebo | 1002 | |
| 10009 | 10009 | B | Placebo | 1003 | |
| 10010 | 10010 | B | Placebo | 1003 | |
| 10011 | 10011 | A | Active | 1003 | |
| 10012 | 10012 | A | Active | 1003 | |
| 10013 | 10013 | B | Placebo | 1004 | |
| 10014 | 10014 | A | Active | 1004 | |
| 10015 | 10015 | B | Placebo | 1004 | |
| 10016 | 10016 | A | Active | 1004 | |
| 10017 | 10017 | B | Placebo | 1005 | |
| 10018 | 10018 | A | Active | 1005 | |
| 10019 | 10019 | A | Active | 1005 | |
| 10020 | 10020 | B | Placebo | 1005 |
The assignment of subjects to the randomization records in the IRT is simple! At randomization, the IRT identifies the next available record (based Sequence Number order) and assigns to the subject. The subject’s ID is permanently linked to their assigned Randomization Number and Treatment.
Looking at the Central Randomization design in the Example 1 list, the 1st, 2nd, 3rd subjects are assigned to Treatments A, A, and B, respectively. The 4th subject will be assigned to Treatment B, which will complete the block. When all 20 subjects are assigned, 5 blocks will be completed and Treatment allocation ratio of 1:1 will be maintained at the overall Study-Level.
What if a Clinical Trial Requires Sub-Group Level Treatment Arm Balance?
If Treatment effect differences are expected across certain subject sub-groups, then the randomization design may need to maintain the Treatment Balance within specified subject sub-groups. The clinical trial’s protocol defines these sub-groups as Stratification Factors. This sub-group level balance can be achieved within a Stratified Blocked Randomization List.
For an example, assume a protocol’s randomization design specifies:
- Treatments: 2 (Active vs. Placebo)
- Treatment Allocation Ratio: 2:1
- Sample Size: 90
- Stratified by:
- Prior Treatment (Yes vs. No)
- Symptom Score: (1 vs. 2 vs. 3)
Based on the above details, approximately 60 subjects will need to be assigned to Active, and 30 subjects assigned to Placebo. Within each cross-combination of the protocol’s Stratification Factor Levels (defined as Stratum), the blocks should maintain the 2:1 Treatment allocation ratio. The Randomization List for this study can be designed with a Block Size of 6 (with 4 Active and 2 Placebo records) and the below Stratum definitions:
| Stratum | Prior Treatment | Symptom Score | Stratum Description |
| 1 | Yes | 1 | Prior Treatment: Yes; Symptom Score: 1 |
| 2 | Yes | 2 | Prior Treatment: Yes; Symptom Score: 2 |
| 3 | Yes | 3 | Prior Treatment: Yes; Symptom Score: 3 |
| 4 | No | 1 | Prior Treatment: No; Symptom Score: 1 |
| 5 | No | 2 | Prior Treatment: No; Symptom Score: 2 |
| 6 | No | 3 | Prior Treatment: No; Symptom Score: 3 |
Each Stratum is pre-allocated its own set of randomized blocks within the Randomization List, which essentially creates a sub-list for each Stratum. Example 2 below shows the 1st block in the Stratum 1 sub-list and the 1st block in the Stratum 6 sub-list.
Example 2: Stratified Randomization List with Blocks Pre-Allocated to Stratum
| Sequence Number | Randomization Number | Stratum | Stratum Description | Treatment Code | Treatment Description | Block Number | SubjectID |
| 10001 | 10001 | 1 | Prior Treatment: Yes; Symptom Score: 1 | A | Active | 1001 | 1 |
| 10002 | 10002 | 1 | Prior Treatment: Yes; Symptom Score: 1 | A | Active | 1001 | |
| 10003 | 10003 | 1 | Prior Treatment: Yes; Symptom Score: 1 | B | Placebo | 1001 | |
| 10004 | 10004 | 1 | Prior Treatment: Yes; Symptom Score: 1 | B | Placebo | 1001 | |
| 10005 | 10005 | 1 | Prior Treatment: Yes; Symptom Score: 1 | A | Active | 1001 | |
| 10006 | 10006 | 1 | Prior Treatment: Yes; Symptom Score: 1 | A | Active | 1001 | |
| … | … | … | … | … | … | … | … |
| 60001 | 60001 | 6 | Prior Treatment: No; Symptom Score: 3 | B | Placebo | 6001 | 2 |
| 60002 | 60002 | 6 | Prior Treatment: No; Symptom Score: 3 | A | Active | 6001 | |
| 60003 | 60003 | 6 | Prior Treatment: No; Symptom Score: 3 | A | Active | 6001 | |
| 60004 | 60004 | 6 | Prior Treatment: No; Symptom Score: 3 | A | Active | 6001 | |
| 60005 | 60005 | 6 | Prior Treatment: No; Symptom Score: 3 | A | Active | 6001 | |
| 60006 | 60006 | 6 | Prior Treatment: No; Symptom Score: 3 | B | Placebo | 6001 | |
| … | … | … | … | … | … | … | … |
To randomize subjects, the IRT first determines the subject’s Stratum, then identifies that Stratum’s sub-list and assigns the next sequential record. If the 1st subject has Prior Treatment = Yes and Symptom Score = 1, then they are assigned to Randomization Number 10001 and Treatment A. If the 2nd subject has Prior Treatment = No and Symptom Score = 3, then they are assigned to Randomization Number 60001 and Treatment B. As subjects are randomized within each Stratum, the blocks assignments are completed and the ratio of 2:1 is sustained.
Is this the only way IRT can randomize subjects within Stratification Levels?
NOPE!
The pre-allocation of blocks to stratum demonstrated above is the most common method for stratified randomization, but there are situations where pre-allocation is not the best fit.
When Site is a Stratification Factor, it is highly possible that more Sites are added mid-study. If pre-allocating blocks to Site (creating a sub-list for each Site), then each time a Site is added in the IRT, a new list would also be needed. This incurs unnecessary downtime and subsequent costs.
To avoid this headache, utilize On-Demand Allocation of Blocks to Sites! The list is generated in the same way as the Central Randomization List in Example 1, with no blocks pre-allocated to any specific Site. Then the IRT allocates blocks to each Site On-Demand.
As an example, assume a protocol’s randomization design specifies:
- Treatments: 2 (Active vs. Placebo)
- Treatment Allocation Ratio: 1:1
- Sample Size: 100
- Stratified by: Site
A Blocked Randomization List with a Block Size of 4 is generated without any blocks pre-allocated to Sites. At Randomization, the IRT first checks if any blocks with available records exist for the subject’s Site. If no, then the IRT identifies / assigns the next set of available Block(s) to the subject’s Site and assigns the 1st record to the subject. If yes, then the subject is assigned to the next available record within the block(s) allocated to their Site.
The number of blocks to assign to each Site can be set based on each study’s preference. For ease, the example will demonstrate allocating just 1 block at a time.
Assume the 1st 3 subjects are at the following Sites:
- SubjectID=1, Site 1234
- SubjectID=2, Site 3232
- SubjectID=3, Site 1234
Since the 1st subject is at Site 1234, the 1st Block (1001) is assigned to Site 1234. SubjectID = 1 is assigned to the 1st record in that Block (Randomization Number 10012, Treatment A). The 2nd subject is at Site 3232, the IRT assigns the 2nd Block (1002) to the Site and 1st record in that Block (Randomization Number 10006, Treatment B) to SubjectID = 2. The 3rd subject is at Site 1234, which has records available for assignment, thus the 2nd record in Block 1001 is assigned (Randomization Number 10004, Treatment B) to SubjectID = 3.
Example 3: Stratified Randomization with Blocks Allocated On-Demand to Stratum (Site)
| Sequence Number | Randomization Number | Treatment Code | Treatment Description | Block Number | Site | SubjectID |
| 100001 | 10012 | A | Active | 1001 | 1234 | 1 |
| 100002 | 10004 | B | Placebo | 1001 | 1234 | 3 |
| 100003 | 10002 | B | Placebo | 1001 | 1234 | |
| 100004 | 10001 | A | Active | 1001 | 1234 | |
| 100005 | 10006 | B | Placebo | 1002 | 3232 | 2 |
| 100006 | 10011 | B | Placebo | 1002 | 3232 | |
| 100007 | 10009 | A | Active | 1002 | 3232 | |
| 100008 | 10007 | A | Active | 1002 | 3232 | |
| 100009 | 10005 | B | Placebo | 1003 | ||
| 100010 | 10008 | A | Active | 1003 | ||
| 100011 | 10003 | A | Active | 1003 | ||
| 100013 | 10010 | B | Placebo | 1003 |
As shown above, this approach sets up the Site stratification within the Blocked Randomization List On-Demand. In this design, the IRT can allocate blocks to new Sites (or even Stratum!), without having to generate subsequent Randomization Lists.
How Should Randomization Numbers be Ordered in the Randomization List?:
For simplicity, in the Examples 1 and 2, the Randomization Number is ordered sequentially and equal to the Sequence Number. However, it may be necessary for Randomization Numbers to appear in random (Scrambled) order for blinding purposes. Example 3 (On-Demand Allocation of Blocks to Sites) warrants Scrambled Randomization Numbers since it involves assigning a single block at a time. Scrambling the Randomization Numbers prevents anyone from identifying the Block Size. In the example, if the Randomization Numbers were ordered sequentially, study personnel may be able to figure out that Randomization Numbers are assigned in sets of 4, which is equal to the block size. To scramble or not to scramble should be agreed upon by sponsor’s Biostatistical Representative and the IRT List Generators.
Important: The Block Size is an Unblinding Parameter that should only be known to study personnel involved in the design and implementation of the Randomization List. Knowledge of the block size can lead to potential Selection Bias!
Final Thoughts:
The IRT randomization list approaches discussed in this article are just the surface of randomization possibilities! More complex methods for randomization are achievable through IRT (e.g., Covariate Adaptive Randomization (minimization), Target Adjusted Algorithms, Hierarchal Algorithms, Adaptive Designs, Master Protocols, etc.).
