Clinical trial planning starts with defining the question the study needs to answer. In this QCast episode, co-hosts Jullia and Tom consider how that question shapes the treatment objective, study design, patient population, endpoints and statistical approach. They explain why decisions such as choosing between superiority and non-inferiority, setting visit windows or defining the comparator cannot be made in isolation.
The practical challenge within clinical trial planning and design is keeping those decisions aligned with what sites and participants can realistically deliver. Eligibility criteria that appear reasonable individually may create an unworkable recruitment pathway when combined. Changes to visit frequency or assessment burden may affect dropout assumptions, while vague endpoint definitions can lead to inconsistent data collection. Addressing these issues early gives study teams more scope to revise the design before they become protocol amendments, recruitment delays or analysis problems.
The research question should define the population, comparator, outcome and assessment period clearly enough to guide the rest of the design. A study assessing symptom reduction at 12 weeks answers a different question from one assessing whether symptom control is maintained for a year. That difference can change the endpoint timing, visit schedule, follow-up period and analysis.
Sample size depends on assumptions such as the expected treatment effect, variability or event rate, power, allocation ratio and anticipated dropout. These assumptions need to be revisited when the protocol changes. Adding visits or more demanding assessments, for example, may increase participant burden and dropout, leaving fewer evaluable patients than the original calculation allowed for.
Feasibility involves more than estimating the number of potentially eligible patients. Study teams need to consider how eligibility criteria work in combination, whether sites have the required capabilities and how participants will experience the full visit schedule. Understanding the specific cause of a recruitment constraint helps teams decide whether to adjust assessments, add sites, extend recruitment or reconsider another part of the design.
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
Now, clinical trial planning can seem like a really broad topic. So where should a study team actually begin?
Jullia
They should start with the research question, and this should be specific enough to guide those impactful design decisions. The team needs to define who the treatment is for, what it’s being compared with, which outcome matters and the period over which that outcome will be assessed. That should lead to a clear treatment objective that states more precisely which effect the trial will estimate.
Tom
Can you give an example of how a small change in that question affects the trial?
Jullia
A study looking at whether a treatment reduces symptoms at 12 weeks is different from one asking whether it maintains symptom control over a year. The treatment and patient population might be the same, but the endpoint timing, visit schedule, follow-up and analysis could all change.
Tom
There’s sometimes pressure to start with a preferred design and make the research question fit around it. What’s the risk there?
Jullia
You can end up running a well-organised trial that doesn’t answer the decision the development team actually needs to make. Perhaps the endpoint is convenient to collect but doesn’t capture the clinically relevant effect. Or the comparator supports one type of claim when the intended objective requires another.
Tom
Once that objective is clear, how does it shape the choice between designs such as superiority or non-inferiority?
Jullia
A superiority trial aims to show that one treatment performs better than another. A non-inferiority trial asks whether a treatment is not unacceptably worse than an established comparator. Equivalence studies assess whether the difference stays within predefined limits. Those are distinct questions, so they require different assumptions, analyses and, potentially, very different sample sizes.
Tom
Could a technically interesting design still be the wrong choice?
Jullia
Absolutely. Adaptive, crossover, factorial and multi-arm designs can all be valuable, but only where they suit the treatment, condition and objective. A crossover design, for example, may work when treatment effects are reversible and carryover can be controlled. It would be much harder to justify where the effect is permanent or the disease changes substantially over time.
Tom
That brings us to the people entering the trial. How closely should the study population reflect the eventual patient population?
Jullia
Closely enough that the results remain relevant, but the population also has to be recruitable. If a treatment is intended for adults with moderate-to-severe disease, filling the study mainly with patients who have mild disease may increase recruitment but weaken the value of the evidence. The scientific target and the patients that sites can realistically identify need to be considered together.
Tom
So a criterion can look reasonable in isolation but create problems when combined with everything else?
Jullia
Exactly. A patient might meet the disease criteria but fail because of a washout period, a comorbidity or a required test that isn’t routinely available. Every added laboratory threshold, prior-treatment condition or medication restriction can reduce the available population, so the team needs to review the complete eligibility pathway, not each criterion separately. A quick discussion with investigators can reveal combinations that seemed workable on paper but rarely occur in clinical practice.
Tom
Endpoints can cause similar difficulties. What makes an endpoint suitable?
Jullia
It should reflect the clinical objective and define the outcome precisely. Change from baseline at a fixed visit answers a different question from time to first meaningful improvement, even if both use the same underlying measure. The endpoint also needs to state how it’s assessed, when it’s collected and what counts as an event or response.
Tom
What happens if those details remain vague during planning?
Jullia
Different sites may interpret the assessment differently, or the visit schedule may not support the intended analysis. Suppose the primary endpoint is measured at week 12, but operational flexibility allows that assessment across a wide window. The team then has to consider whether timing differences could affect interpretation and how those observations will be handled statistically.
Tom
A common misconception is that secondary endpoints provide a fallback if the primary endpoint fails. How should teams think about them instead?
Jullia
Secondary endpoints should provide planned supporting evidence or examine additional effects. They’re not there to rescue an unsuccessful primary analysis after the event. Their role, statistical testing and relationship to the primary objective should be considered before the trial starts, including any multiplicity implications.
Tom
Comparator choice can also feel straightforward until blinding enters the discussion. How do those decisions interact?
Jullia
The comparator creates the reference against which the treatment effect is interpreted, whether that’s placebo, an active treatment, standard of care or another dose. But its route and appearance can affect whether blinding is possible. Comparing an infusion with an oral treatment, for instance, may require a double-dummy approach if maintaining the blind is important.
Tom
And if blinding simply isn’t realistic?
Jullia
Then the team should identify which sources of bias can still be controlled. An open-label study might use blinded endpoint assessment or independent outcome adjudication. Randomisation can reduce systematic differences between groups, while allocation concealment prevents the next assignment being known before a participant is enrolled. Those mechanisms address related but different risks.
Tom
At what point should statisticians become involved? There’s still a view that sample size comes after the main protocol decisions.
Jullia
Statistical input needs to begin much earlier. A sample-size calculation can show whether the proposed effect, endpoint and population are realistic within the available time and resources. If the calculation produces a study far larger than the recruitment pool can support, the design needs reconsideration before the protocol is finalised.
Tom
What assumptions are doing most of the work in that calculation?
Jullia
It depends on the endpoint, but common assumptions include the expected treatment effect, variability or event rate, power, allocation ratio and anticipated dropout. A non-inferiority study also depends heavily on its chosen margin. If any of those assumptions change, the sample-size calculation may no longer represent the trial being planned.
Imagine the sample size was calculated using a week-12 endpoint and an expected dropout rate based on relatively light participant burden. The protocol is then revised to include more visits and invasive assessments, but the calculation isn’t revisited. The added burden could increase dropout, leaving fewer evaluable participants than planned.
Tom
Where does the estimand fit into this?
Jullia
An estimand defines the treatment effect the study is trying to estimate. It brings together the population, treatment condition, outcome and the way events after randomisation will be handled. Those events might include treatment discontinuation, rescue medication, switching therapy or death.
Tom
Why can’t those events simply be dealt with when the data is analysed?
Jullia
Because the chosen approach changes the question being answered. If a patient stops treatment but continues attending visits, the team needs to know whether their later outcomes remain relevant to the effect of interest. Deciding after seeing the results introduces avoidable subjectivity and may leave the clinical objective disconnected from the analysis.
Tom
A design can still be scientifically sound and fail operationally. How should feasibility challenge the assumptions?
Jullia
Feasibility should test whether the trial can be delivered in the real settings where participants will be recruited and followed. That includes the available patient pool, expected screen failures, competing studies and site capacity. It should also consider whether sites have the required equipment, pharmacy arrangements and access to specialist assessments.
Tom
Patient burden deserves attention here too, doesn’t it?
Jullia
It does. Long visits, repeated blood sampling, travel, fasting or extensive questionnaires can affect whether people enrol and remain in the study. A demanding schedule may look manageable when each procedure is reviewed separately, but patients experience the full visit. That can influence adherence, missing data and dropout.
Tom
Suppose feasibility suggests the original plan won’t recruit. Is widening the eligibility criteria the obvious response?
Jullia
It’s one option, but the team should first understand the source of the constraint. The issue may be a rare biomarker, excessive screening requirements or too few capable sites rather than the main disease criteria. Depending on the cause, the answer could be reducing visits, adding sites, extending recruitment or reconsidering endpoint timing.
Tom
Before we finish, what should listeners take away when they’re reviewing a study plan?
Jullia
First, keep the research question specific and use it to test every major design decision. Second, involve statistical and operational perspectives early, while assumptions can still be challenged without major rework. And finally, treat feasibility as part of design quality, because a methodologically strong protocol only produces useful evidence if sites and participants can carry it out.
With that, we’ve come to the end of today’s episode on clinical trial planning and design. 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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