In this QCast episode, co-hosts Jullia and Tom explore adaptive randomisation in clinical trials and why it can be an attractive alternative to fixed allocation when evidence is accumulating during conduct. They clarify what adaptive randomisation means in practice, including response-adaptive randomisation, where allocation probabilities shift as outcomes emerge, and covariate-adjusted response-adaptive randomisation, where baseline patient characteristics and biomarkers can influence assignment for the next participant. The conversation focuses on what these approaches are designed to achieve, what can go wrong if adaptation outpaces the data, and the practical and governance foundations needed to keep results interpretable and trial conduct robust.
What Adaptive Randomisation Is and Why It Matters
Adaptive randomisation allows treatment allocation probabilities to change during a trial using accumulating information, while preserving the core benefits of random assignment. It is often considered when teams want to learn efficiently, reduce exposure to less favourable options, or reflect credible expectations that treatment effects differ across subgroups.
How Adaptive Randomisation Works in Practice
Response-adaptive randomisation updates allocation based on observed outcomes across arms. When patient characteristics are incorporated, covariate-adjusted response-adaptive randomisation uses baseline covariates and biomarkers to estimate which treatment may be more suitable for a participant with a given profile, then randomises with a controlled tilt rather than a fixed ratio. Designs commonly include safeguards such as an initial burn-in period and bounds on allocation to avoid extreme imbalance and maintain reliable estimation.
Limitations, Governance, and Best Practices
Adaptive approaches add operational and inferential complexity. Delayed endpoints can make it hard to adapt on the right signal at the right time, and time trends in recruitment or background care can complicate interpretation when allocation is also shifting. To remain defensible, teams should prespecify decision rules, test operating characteristics through realistic simulation, and put strong controls around interim information and randomisation delivery so the added complexity does not undermine integrity or clarity of the final answer.
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.
Tom
So today we’re talking about adaptive randomisation in clinical trials. Some people might think it just means changing the randomisation ratio mid trial, but what does it actually mean and problem is it actually trying to solve?
Jullia
So at its simplest, adaptive randomisation means that the probability of being assigned to each treatment arm can change as the trial progresses, using information that’s accumulating. In a conventional randomised controlled trial, you might allocate fifty-fifty throughout, because you assume equipoise, which means you genuinely don’t know which treatment is better. But in some settings, that assumption is under pressure. As outcomes start to point in a direction, there’s often a pull to avoid continuing to allocate large numbers of participants to an option that looks less favourable. Response-adaptive randomisation is one way to address that. The idea is to learn during the trial and tilt future allocations towards the treatment that appears to perform better, while still maintaining enough randomness to preserve interpretability and reduce bias.
Tom
And where do patient characteristics come into this? Because sometimes people mix up response-adaptive randomisation with methods that just balance baseline factors.
Jullia
So there are a few related ideas that get bundled together. One is adapting to responses, where the trial updates allocation based on how participants are doing on each arm. Another is adapting based on patient characteristics, which are covariates measured before treatment, like age, weight, disease severity, or biomarkers. You can combine the two in covariate-adjusted response-adaptive randomisation, or CARA. In that setup, the model doesn’t just ask, “Which arm looks better overall?” It asks, “Which arm looks better for someone like this next participant?” That’s where the personalised medicine angle comes in. If treatment benefit differs by subgroup, adaptive randomisation can, in principle, allocate more participants to the option predicted to work best for their own specific profile, rather than applying a fixed rule for everyone.
Tom
Now when we say covariates and biomarkers, what should teams have in mind, and what’s the difference between prognostic and predictive markers here?
Jullia
So a covariate is any baseline characteristic that can influence outcome. This can be continuous, like age, binary, presence or absence of a feature, or categorical, like disease subtype. A biomarker is a particular type of covariate that’s measured objectively and can act as an indicator of biological processes, disease processes, or response to a therapy. The prognostic versus predictive distinction matters a lot for CARA-style thinking. A prognostic marker is associated with outcomes regardless of which treatment someone receives. It tells you about underlying risk or likely trajectory. A predictive marker, on the other hand, changes the treatment effect. It helps you identify who benefits more from treatment A versus treatment B. If you want patient-characteristics-driven adaptation to do something meaningful, you need a plausible basis for prediction, not just risk stratification.
Tom
Another common theme that comes up is using historic or prior data. How does historic information fit into adaptive randomisation without making the design hard to defend?
Jullia
So historic data already influences trials in a fairly standard way, even if people don’t always frame it that way. It feeds into assumptions about treatment effects, variability, and sample size planning. With adaptive randomisation, you can go further and let prior information inform early allocation decisions, then update those decisions with current trial data as it accumulates. The key is to treat historic data as supportive rather than as a replacement for contemporaneous randomisation. One practical approach is to down-weight historic information relative to current participants, and to adjust that weight depending on comparability. If the historic patients look similar to the current participants on relevant covariates, you give that information more influence. If they look different, you reduce its impact so the trial learns primarily from what’s happening now.
Tom
Okay, so what does something like CARA actually do, step by step, without getting lost in equations?
Jullia
Think of it as a loop that runs each time a new participant is ready to be randomised. First, you use the data you have, which might include prior information and the outcomes observed so far, to predict what outcome you’d expect for the next participant under each treatment option. Second, you use the participant’s baseline covariates, because those are the inputs that let the prediction be individualised rather than purely average. Third, you translate those predictions into allocation probabilities. That part matters because you still randomise, but you might assign the participant to their predicted best option with a higher probability than fifty per cent. Plus, the size of that tilt can evolve as evidence accumulates. Fourth, you observe outcomes and feed them back into the model. Then you repeat for the next participant. Many designs also include a burn-in period with equal allocation to stabilise early estimates before you adapt more aggressively.
Tom
So that’s the appealing side. But what are the main risks and limitations teams need to be honest about?
Jullia
There are several, and they’re not just statistical. Statistically, if you skew allocations too much, you can end up with imbalanced sample sizes that reduce precision for key comparisons, especially if the true effect is smaller than expected or differs across subgroups. Time trends are another risk. If background care, recruitment patterns, or patient mix shift over the trial, and you’re simultaneously changing allocation ratios, you can end up confounding treatment effects with calendar time. Operationally, response-adaptive methods often need outcomes quickly enough to update allocation rules in a meaningful way. If the primary endpoint takes months, you may be adapting based on incomplete signals, which can mislead the algorithm. And then there’s integrity. If people involved in conduct can infer how randomisation is moving, it can introduce subtle bias in who gets recruited, how outcomes are assessed, or how deviations are handled. That’s why current guidance expectations generally point towards prespecification, clear governance around interim information, and upfront simulation to understand how the design behaves under realistic conditions.
Tom
So if a team is considering adaptive randomisation, what should they build into the protocol and delivery model so it’s robust?
Jullia
I’d start by being clear on the objective. Are you trying to improve ethics by allocating more participants to the better-performing option, improve efficiency by learning faster, or improve targeting by matching treatments to subgroups? Those are different goals, and the design choices follow from them. Next, prespecify the decision rules. That includes when updates happen, what data triggers an update, how delayed outcomes are handled, and what limits apply, like minimum and maximum allocation probabilities, so you don’t drift towards near-deterministic assignment. Then, run simulations early using plausible assumptions about recruitment, endpoint timing, and treatment effects across covariates, because that’s how you understand power, error rates, and expected allocation patterns. Finally, plan operational controls. Keep access to unblinded interim information tightly restricted, use a robust randomisation system with appropriate audit trails, and make sure the statistical analysis plan and data flow can support updates without confusion or versioning issues.
Tom
Now what are some of the quick wins and challenges to keep in mind?
Jullia
First, be clear what you’re optimising, whether that’s ethics, efficiency, or subgroup matching. Second, don’t let the algorithm run away early, so use a burn-in and cap how far allocation can tilt. Third, make sure the adaptation is driven by a signal you can get in time, not by endpoints that arrive too late to guide decisions. Fourth, prespecify the update rules and test them in simulation so you understand what the design does under realistic scenarios. Fifth, protect integrity by limiting access to interim trends and keeping the randomisation process controlled and auditable.
Tom
Now can you give an example of how patient characteristics might change what “better” looks like?
Jullia
Sure. So imagine a setting where the average treatment effect hides meaningful differences across baseline subgroups. If you have a biomarker or clinical profile that’s plausibly predictive, the design can use that information at the moment of randomisation. For the next participant, the model estimates which treatment is more likely to be beneficial given their characteristics, then shifts allocation probabilities accordingly while maintaining randomisation. Over time, that can steer more participants in certain profiles towards the option that appears more suitable for them, while still keeping enough allocation to other arms to learn reliably. The crucial condition is that the covariates and how they enter the model are planned from the start. If you start chasing patterns after the fact and then build adaptation around them, you’re more likely to fit noise and less likely to produce a defensible answer.
Tom
So when is adaptive randomisation a genuinely good fit and when should teams step back and perhaps consider a simpler approach?
Jullia
It’s a good fit when the science and the practicalities line up. It can be attractive in rare diseases, where every participant matters and there’s strong motivation to learn efficiently while treating trial participants responsibly. It can also make sense when there’s credible prior reason to expect treatment effects vary across measured patient characteristics, and those characteristics can be captured consistently. But if endpoints are slow, if recruitment is fragile, if your operational controls are weak, or if the trial’s main purpose is a straightforward confirmatory comparison, a fixed randomisation scheme is often more robust. In some cases, you can achieve much of what teams want through simpler methods, like stratified randomisation to balance key factors, or a fixed unequal allocation ratio, without introducing the complexity of continuously adapting probabilities.
So to close, adaptive randomisation is about letting allocation shift using accumulating evidence, while still protecting the benefits of random assignment. It can be especially useful when patient characteristics plausibly change who benefits, and you can measure what you need quickly and consistently. But it only earns its keep when the governance, data flow, and analysis plan are strong enough that the added complexity doesn’t blur the answer.
With that, we’ve come to the end of today’s episode on adaptive 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 tuning in, and we’ll see you in the next episode.
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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