In this QCast episode, co-hosts Jullia and Tom examine randomisation in clinical trials—the foundation for fair comparisons and credible results. They explain how randomisation reduces bias, supports blinding, and ensures trial outcomes can be interpreted with confidence. The conversation covers the main approaches, including simple, block, and stratified randomisation, and when more complex methods such as unequal allocation or adaptive designs may be appropriate.
You’ll learn how to select the right randomisation method for your study, plan the allocation strategy in advance, and put in place the safeguards that keep assignments consistent across multiple sites. The episode also highlights the role of centralised systems like Interactive Response Technology, the importance of quality control and archiving, and a case study showing how unequal allocation across 5 arms was successfully implemented. Whether you’re designing a new protocol, reviewing a vendor plan, or supporting trial oversight, this episode provides clear, practical guidance to strengthen the integrity of your study from the outset.
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Key Takeaways
What is Randomisation in Clinical Trials?
Randomisation is the process of assigning participants to treatment groups using a pre-defined chance mechanism. It reduces selection bias, supports blinding, and ensures trial results can be interpreted with confidence. By balancing baseline factors across groups, randomisation provides a valid basis for statistical analysis and strengthens the credibility of study outcomes.
Core Approaches to Randomisation
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Simple Randomisation: Each participant is assigned purely at random; suitable for large trials where group sizes balance naturally.
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Block Randomisation: Ensures groups remain balanced throughout enrolment by using fixed or varying block sizes.
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Stratified Randomisation: Adds control by balancing important factors—such as site, age group, or disease severity—across treatment arms.
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Unequal Allocation and Adaptive Methods: Useful in certain cases but require a clear rationale, larger sample sizes, and careful governance.
Why Planning Matters
By embedding randomisation planning into study design, teams can:
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Specify Allocation Clearly: Define ratios, block sizes, and stratification factors in advance.
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Support Multi-Centre Trials: Use centralised systems like Interactive Response Technology to maintain consistency across sites.
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Safeguard Blinding: Keep block sizes undisclosed and separate the teams generating randomisation from those analysing data.
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Enable Interim Monitoring: Predefine checks so drift or imbalance can be identified early.
Operational Essentials for Implementation
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Write a randomisation specification early, with rationale for each design choice.
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Produce dummy schedules to confirm outputs without revealing allocations.
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Protect confidentiality through secure storage, encrypted transfers, and restricted access.
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Define and test an emergency unblinding procedure.
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Apply quality control checks and archive specifications, code, and outputs for audit readiness.
Common Pitfalls to Avoid
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Treating randomisation as a simple administrative step rather than a critical trial design element.
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Choosing block or stratified methods without adjusting for multi-centre enrolment realities.
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Allowing predictability in block sizes, which risks unblinding.
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Skipping routine monitoring of group counts and site-level allocations.
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Failing to archive randomisation documentation, making it difficult to answer questions during audits.
Full Transcript
Jullia
Welcome to QCast, the show where biometric expertise meets data-driven dialogue. I’m Jullia.
Tom
I’m Tom, and in each episode, we dive into the methodologies, case studies, regulatory shifts, and industry trends shaping modern drug development.
Jullia
Whether you’re in biotech, pharma, or life sciences, we’re here to bring you practical insights straight from a leading biometrics CRO. Let’s get started.
Jullia
Today we’re discussing randomisation in clinical trials, which in short, is how participants are assigned to treatment groups by a pre-defined chance process, and why that matters for the credibility of the results. We’ll be laying out the rationale, common methods, operational controls, and even provide some insight into a case study. Our aim is to translate these points into a straightforward, spoken overview you can use when drafting or reviewing protocols, vendor plans, and data monitoring materials. We’ll cover why randomisation reduces bias and supports blinding, how simple, block, and stratified approaches work, when more complex methods and unequal allocation are justified, and the safeguards needed in multi-centre trials.
Tom
Thanks Jullia. To frame it: randomisation is central to the validity of a controlled trial. By using a chance mechanism to assign participants, we reduce selection bias and make treatment groups comparable at baseline, so observed differences are more plausibly due to the intervention rather than pre-existing imbalances. That design choice also supports double blinding. This means neither participants nor investigators can infer the next allocation from someone’s characteristics, and it aligns with ethical principles when there is genuine uncertainty about which treatment is better. When randomisation is properly implemented, standard statistical methods rest on firmer ground, and the evidence is more generalisable to the population of interest.
Tom
So let’s start by focusing on the fundamentals: many people think of randomisation as a simple coin toss, but the practice includes several structured options. Could you walk us through the three core approaches—simple, block, and stratified randomisation? I’d like the listeners to understand when simple randomisation is acceptable, when to use blocks to prevent drift in group sizes during staggered enrolment, and how stratification helps balance key baseline factors such as site, age group, or disease severity.
Jullia
Of course. So, simple randomisation assigns each participant purely at random, typically via a random number generator. In large samples, groups tend to balance, but smaller trials can finish lopsided. Block randomisation tackles that by fixing balance in short runs. You select a block size compatible with your intended allocation ratio, enumerate all valid sequences within the block, randomly pick one, and assign participants in that order. This keeps group counts aligned over time, which is useful when recruitment is uneven. Stratified randomisation adds control for important baseline factors: you define strata—often by site or a clinical variable—and perform randomisation separately within each stratum, so those characteristics are balanced across arms rather than only overall. These three approaches cover most needs in early planning.
Tom
Next, let’s address ‘complex’ methods and unequal allocation. There are scenarios where you might diverge from one-to-one assignment, say, two-to-one or use data-driven approaches such as minimisation or adaptive randomisation. For listeners who advise on design: what are the typical reasons to use unequal allocation, what are the trade-offs, and how should teams think about methods that adjust allocation probabilities in real time?
Jullia
Unequal allocation is chosen for several reasons: to collect more information on the experimental arm, to reflect ethical considerations, to manage cost differences, or to account for anticipated drop-out rates between groups. The main trade-off is statistical power. For the same total sample, moving away from one-to-one usually requires a larger total to achieve the same power. On methods: minimisation updates the next assignment to reduce imbalance in pre-specified factors across groups. Adaptive randomisation adjusts probabilities in response to accumulating outcomes, favouring the arm that appears more effective, while preserving the overall inferential framework. While there’s more to say about these methods, they’re a little beyond the scope of today’s episode. However, it’s still important to note they require careful governance and pre-specification to avoid bias.
Tom
Let’s move onto process and governance. Good design needs disciplined execution. Some practical controls include planning and documentation, confidentiality of schedules, separating the team that generates allocations from the analysis team, emergency unblinding procedures, and quality control with proper archiving. Could you outline those steps simply, so teams know what to expect from a thorough randomisation plan?
Jullia
First things first, start with a written specification: target allocation ratio, intended block sizes or ranges, planned stratification factors, and the number of centres. Include the format of outputs. For example, group labels and any integration with kit lists. Produce a dummy schedule to confirm layout without revealing true assignments. Protect confidentiality by storing schedules in secure systems with restricted access, using encrypted transfers, and sharing passwords separately. Next, keep schedule generation independent from analysis and reporting functions. Define an emergency unblinding process for individual cases, with clear roles and audit trails. Apply rigorous quality control by validating software outputs against the specification and archive schedules, code, and QC records with sensible metadata so they are traceable and retrievable later.
Tom
Moving onto the multi-centre reality. Modern trials often operate across several sites. Central randomisation is usually managed using Interactive Response Technology, sometimes called Interactive Web Response Systems. These systems keep allocations consistent across all sites, apply stratification and blocking rules automatically, and reduce manual errors. Could you expand on how these systems help, and then introduce the case study we’ll be covering today?
Jullia
So, centralised systems implement the pre-specified rules uniformly across all centres in real time. They manage stratification by site or other factors, enforce block structures, and keep block sizes undisclosed to site staff to minimise predictability. Because the platform handles assignment and audit logging, it reduces protocol deviations and supports supply coordination. Turning to the case study: the team designed a phase 2 proof-of-concept trial with 5 arms and unequal allocation: 3 participants each to low, mid, and high dose; 2 to a positive control; and 2 to a negative control. Randomisation took place at 2 sites, stratified by site and a binary baseline variable. The total sample was 234 participants, allocated as 54, 54, 54, 36, and 36. The mechanism aimed to keep the 3:3:3:2:2 ratio close at a planned halfway analysis.
Tom
So that design uses a clear block structure. For our listeners who like to see the moving parts, here’s how it works: adding 3+3+3+2+2 gives a block size of 13, which is then divided into 3 sub-blocks. Could you describe those sub-blocks and the two-level randomisation, and mention the practical touches about list sizes per stratum and pharmacy coordination?
Jullia
So the main block of 13 was split into 3 sub-blocks. Sub-block 1 assigns 1 participant to each arm consisting of low dose, mid dose, high dose, positive control and negative control. Sub-block 2 assigns 1 participant to low dose, mid dose, high dose and positive control with none to negative control. Sub-block 3 assigns 1 participant to low dose, mid dose, high dose and negative control with none to positive control. Then, two levels of randomisation were applied: first, randomise the order of treatments within each sub-block; second, randomise the order of the sub-blocks within the 13-slot block. This preserves the overall ratio while keeping allocations unpredictable. Lists were generated for 234 participants within each stratum, so the trial could continue even if recruitment was concentrated in a single stratum. Pharmacies received schedules and prepared treatments as needed, which limited excess supply and waste.
Tom
So, what should our listeners do next? Suppose someone is drafting a protocol or reviewing a vendor plan. What immediate actions improve the quality of randomisation without adding unnecessary complexity? I’m thinking of a short checklist that covers design choices, the operational safeguards you’ve described, and the routine checks that spot drift early.
Jullia
Here’s a focused set of actions. First, document the allocation strategy early: ratio, block sizes or ranges, and stratification factors, each with a brief rationale. Second, request or produce a dummy schedule and confirm the exact output format and data flows, including any integration with supply or kit lists. Third, mandate centralised randomisation for multi-centre trials, stratifying by centre and keeping block sizes undisclosed. Fourth, separate roles: list the minimal set of people with access to real schedules and keep schedule generation independent from analysis. Fifth, define routine monitoring such as having weekly counts by arm within each stratum and by site, with pre-agreed thresholds for follow-up. Sixth, if using unequal allocation, ensure the sample-size plan reflects the power trade-off. Seventh, finalise quality control and archiving procedures so that code, logs, and schedules are traceable. These steps make the design transparent and the operations reproducible.
Tom
Before we wrap up, let’s summarise the key points. One: randomisation reduces selection bias, supports blinding, and underpins valid inference. Two: choose the method to fit the trial—simple for large samples; block to maintain balance over time; and stratified to balance important factors across arms. Three: complex methods and unequal allocation have legitimate uses but require clear justification and careful governance. Four: execution matters—plan, protect confidentiality, separate roles, define emergency unblinding, apply quality control, and use centralised systems in multi-centre settings. That’s the basis for reliable, auditable evidence from a trial.
Jullia
Absolutely. Treat the randomisation plan as a testable specification. Make it explicit, implement it in a controlled system, and monitor it routinely. And with that, we’ve come to the end of today’s episode on Randomisation in Clinical Trials. If you found this discussion useful, don’t forget to subscribe to QCast so you never miss an episode, and share it with a colleague. And, if you’d like to learn more about how Quanticate supports data-driven solutions in clinical trials, head to our website or get in touch.
Tom
Thanks for listening, and we’ll see you in the next episode.
About QCast
QCast by Quanticate is the podcast for biotech, pharma, and life science leaders looking to deepen their understanding of biometrics and modern drug development. Join co-hosts Tom and Jullia as they explore methodologies, case studies, regulatory shifts, and industry trends shaping the future of clinical research. Where biometric expertise meets data-driven dialogue, QCast delivers practical insights and thought leadership to inform your next breakthrough.
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