Clinical Trial

How a clinical trial evaluates an intervention

A clinical trial prospectively assigns one or more health-related interventions to human participants and measures the resulting outcomes. This page explains how trial questions, designs, interventions, outcomes, safety procedures and analyses work together to produce evidence about benefits, harms and value.

Clinical trial is a broad category. A trial may be randomised or non-randomised, controlled or single arm, blinded or open label, and designed for early development, confirmatory testing or routine-practice evaluation.

Defining the question before the trial begins

A trial should begin with a clear question that identifies the population, intervention, comparator, outcomes and period of follow-up. These elements determine who can participate, what is delivered and which conclusions the trial can support.

The protocol commonly specifies:

• Population: The participants to whom the research question applies.
• Intervention: The medicine, device, procedure, behavioural programme, diagnostic strategy or service being evaluated.
• Comparator: Placebo, usual care, another active intervention, a different dose or no concurrent comparator.
• Outcomes: The intended measures of benefit, harm and patient experience.
• Timing: The schedule of intervention and outcome assessment.
• Setting: The clinical and geographical context of the trial.
• Estimand: The treatment effect the analysis is intended to estimate.

The trial should answer a question that matters to patients, clinicians or decision makers. A technically successful study can still provide limited value when its population, comparator or outcome does not match the decision that must be made.

What makes a study a clinical trial

A clinical trial assigns participants prospectively to an intervention according to a research protocol. This assignment distinguishes an interventional trial from an observational study in which researchers observe care or exposures without assigning them.

The assigned intervention may include:

• A medicinal product or vaccine.
• A medical or digital device.
• A surgical or diagnostic procedure.
• A behavioural or public-health intervention.
• A rehabilitation or psychological programme.
• A model of healthcare delivery.
• A prevention or screening strategy.
• A combination of interventions.

Assignment does not necessarily mean random assignment. A single-arm study in which every participant receives the experimental intervention is still a clinical trial, but it lacks the protection against confounding provided by randomisation.

Choosing the trial design

The design should match the development stage, intervention, ethical constraints and effect the study is intended to estimate. No single design is appropriate for every clinical question.

Common designs include:

• A parallel-group trial assigns participants to one intervention for the main study period.
• A single-arm trial assigns all participants to the same intervention and may compare results with historical or external information.
• A crossover trial assigns participants to a sequence of interventions.
• A cluster trial assigns groups such as clinics or communities rather than individuals.
• A factorial trial evaluates more than one intervention in the same study.
• A stepped-wedge trial introduces the intervention to clusters in a planned sequence.
• An adaptive trial permits prespecified changes using accumulating evidence while protecting statistical validity.
• A pragmatic trial evaluates an intervention under conditions intended to reflect routine practice.

Design labels do not establish quality. The protocol must explain allocation, comparison, follow-up and analysis in sufficient detail to judge bias and applicability.

How randomisation changes the comparison

Randomisation assigns participants using a chance-based process. When implemented correctly, it helps create treatment groups that are comparable in both measured and unmeasured baseline characteristics.

Non-randomised trials may be necessary in early development, rare conditions or situations where random allocation is impractical or unethical. Their treatment comparisons are more vulnerable to differences in prognosis, time trends and participant selection.

Randomisation should therefore be treated as a design feature rather than as a synonym for clinical trial. Conclusions about causality should match the actual allocation method.

Concealing allocation and blinding participants

Allocation concealment prevents people enrolling participants from predicting the next assignment before enrolment. Blinding prevents participants, clinicians, assessors or analysts from knowing the assigned intervention after allocation.

These safeguards address different biases. Concealment protects selection into groups, while blinding can reduce differences in care, behaviour, outcome measurement and analysis.

Blinding may be impossible for surgery, rehabilitation or service-delivery trials. The design can still use blinded outcome assessment, standardised procedures, objective measures and prespecified analysis to reduce bias.

Stages of clinical development

Trials are often described by phases, particularly for medicines, but phases do not provide a complete description of design or purpose. Development can be non-linear, and devices, procedures and complex interventions may use different pathways.

Broad purposes include:

• Early safety and human pharmacology studies examine tolerability, dosing and biological activity.
• Exploratory studies assess preliminary efficacy, dose response and outcome selection.
• Confirmatory studies test benefits and harms in a larger population using prespecified hypotheses.
• Post-authorisation studies examine longer-term safety, effectiveness, new populations or routine use.

The evidence required depends on the intended claim and decision. An early biological signal does not substitute for confirmatory evidence of patient benefit.

Selecting participants

Eligibility criteria define the study population and protect participant safety. They should be broad enough to answer the intended question while excluding people for whom participation would create unacceptable risk or make interpretation impossible.

Criteria may address:

• Diagnosis and disease stage.
• Age and clinical severity.
• Previous and current treatment.
• Comorbidities and organ function.
• Pregnancy or reproductive considerations.
• Laboratory or biomarker values.
• Ability to follow study procedures.
• Contraindications to the intervention.

Highly restrictive criteria can improve experimental control but reduce applicability to routine care. Exclusions should have a clinical or methodological rationale rather than reflect convenience alone.

Describing the intervention and comparator

The intervention should be specified in enough detail for consistent delivery and replication. The comparator should represent a meaningful alternative for the trial question.

The description should include:

• Intervention components and dose or intensity.
• Mode, timing and duration of delivery.
• Provider qualifications and training.
• Permitted and prohibited co-interventions.
• Adherence and fidelity measures.
• Rules for modification, interruption or discontinuation.
• The content of placebo, usual care or active comparison.

Usual care may differ across centres and change over time. It should be documented rather than treated as an empty or self-explanatory condition.

Selecting trial outcomes

Outcomes should represent the intended benefits and harms and should be measured at clinically meaningful times. The protocol should distinguish primary, secondary and exploratory outcomes before results are examined.

Trial outcomes may include:

• Survival or time-to-event outcomes.
• Symptoms and functioning.
• Patient-reported outcomes.
• Health-related quality of life.
• Clinical events.
• Biomarker or surrogate endpoints.
• Adverse events.
• Resource use and costs.
• Treatment adherence and discontinuation.

The primary outcome drives the central interpretation and often the sample-size calculation. Secondary outcomes add important context but should not be selectively elevated because they produce favourable results.

Biomarker and surrogate endpoints

Biomarker endpoints can show biological activity earlier than direct clinical outcomes. A surrogate endpoint is used as a substitute for how a patient feels, functions or survives and therefore requires evidence that treatment effects on the surrogate predict effects on the patient-relevant outcome.

A treatment can improve a biomarker without improving overall health, particularly when the biological pathway is incomplete or harms offset the expected benefit. Trial reports should state whether an endpoint demonstrates biological response or supports a claim of clinical benefit.

Determining the sample size

Sample-size planning estimates how many participants are needed to address the primary question with adequate precision or statistical power. It should use assumptions that are clinically meaningful and supported by evidence.

Inputs may include:

• The expected outcome rate or variability.
• The treatment effect the trial is designed to detect.
• The significance level and statistical power.
• The allocation ratio.
• Expected loss to follow-up.
• Clustering, repeated measures or multiple primary outcomes.
• The planned analysis method.

A study can be too small to provide a useful estimate or unnecessarily large relative to its question. Feasibility, ethics and statistical requirements should be considered together.

Protecting participants

Clinical trials require independent ethical review, informed consent and continuing attention to the balance of potential benefits and harms. The level of oversight should reflect the intervention and risk.

Participant protection commonly includes:

• A scientifically justified protocol.
• Independent ethics review.
• A clear consent process.
• Privacy and data-protection safeguards.
• Safety monitoring and reporting.
• Rules for treatment interruption or withdrawal.
• Compensation or care for research-related injury where applicable.
• Additional protections for vulnerable populations.
• Independent monitoring when the risk or design warrants it.

Consent is a continuing process rather than a signed form alone. New information that could affect willingness to participate should be communicated appropriately.

Monitoring safety

Safety monitoring records adverse events, serious adverse events and other prespecified risks during and after intervention exposure. Event definitions, collection methods and observation periods should be consistent enough to support comparison.

Safety interpretation should consider:

• Event severity and seriousness.
• Timing in relation to intervention.
• Possible causality.
• Expectedness.
• Baseline disease risk.
• Exposure duration.
• Treatment discontinuation.
• Differences in follow-up between groups.

Trials may be too small or short to identify rare or delayed harms. Post-trial surveillance and observational evidence may therefore remain necessary.

Following participants and handling missing outcomes

Participant flow from enrolment through follow-up should be visible. Discontinuation of treatment, withdrawal from an assessment, loss to follow-up and death should be recorded separately because they have different meanings.

Missing outcomes can bias the result when missingness is related to treatment, prognosis or adverse events. The analysis should report the amount and reasons for missing data, use methods consistent with explicit assumptions and test plausible alternatives.

Increasing the sample size does not correct bias caused by differential loss to follow-up. Retention and outcome collection remain part of trial validity.

Analysing treatment effects

The analysis should follow the protocol and statistical analysis plan and should estimate an effect with uncertainty. A significance threshold alone does not show the magnitude, clinical importance or reliability of the result.

For a binary outcome with risks (p_1) and (p_0), the risk difference is:

$$ Risk\ difference = p_1 - p_0 $$

The relative risk is:

$$ Relative\ risk = \frac{p_1}{p_0} $$

Absolute effects depend on baseline risk and are often more directly useful for clinical and economic decisions. Confidence intervals should accompany effect estimates to show the range of values compatible with the data and model assumptions.

Intention-to-treat and other analysis populations

In a randomised trial, intention-to-treat analysis generally compares participants according to the groups to which they were assigned. It preserves the randomised comparison and often estimates the effect of treatment assignment under the trial conditions.

Per-protocol and as-treated analyses may address adherence or treatment received but can be confounded because post-randomisation behaviour is not randomly assigned. These analyses should be prespecified and interpreted as answering different questions.

In a non-randomised or single-arm trial, intention-to-treat does not create the protection of randomisation. The design and analysis population should be described without implying otherwise.

Registration and transparent reporting

Prospective registration makes key trial information publicly accessible before results are known. Protocol publication, statistical analysis plans and complete results reporting help distinguish planned analyses from later exploratory work.

Transparent reporting should include:

• Registration and protocol identifiers.
• Recruitment dates and trial sites.
• Participant flow.
• Baseline characteristics.
• Intervention and comparator delivery.
• Outcomes for every prespecified group.
• Harms and withdrawals.
• Protocol deviations and amendments.
• Funding and conflicts of interest.
• Access to results and supporting materials where appropriate.

Registration does not guarantee quality, but failure to register or report results can conceal selective methods and contribute to publication bias.

Economic evaluation alongside a trial

A trial can collect patient-level costs, resource use and health outcomes for an economic evaluation. Concurrent collection helps preserve the relationship between intervention exposure, outcomes and service use.

An economic evaluation may include:

• Intervention development and delivery costs.
• Medicines, procedures and monitoring.
• Hospital, community and social-care use.
• Patient and caregiver costs when relevant to the perspective.
• Productivity effects when appropriate.
• Health-related quality of life.
• Quality-adjusted life years.
• Incremental costs and outcomes.
• Uncertainty in joint cost and outcome differences.

Cost data are often skewed and may be missing for reasons related to health. The economic analysis should prespecify costing, perspective, price year, missing-data methods and uncertainty analysis.

When a decision model is still needed

Trial follow-up may be too short to capture long-term benefits, harms and costs. A model can extrapolate outcomes, combine external evidence and compare strategies not included in the trial.

Modelling introduces assumptions about treatment persistence, disease progression, survival, quality of life and future resource use. Trial-based and model-based results should therefore be distinguished, and the effect of extrapolation should be tested.

Applicability to routine care

Trial evidence should be interpreted in relation to the patients, providers and systems facing the decision. Differences between the trial and routine practice may alter effectiveness, safety, adherence, costs or feasibility.

Applicability questions include:

• Does the trial population resemble eligible patients in practice?
• Is the comparator current and relevant?
• Can providers reproduce the intervention intensity and expertise?
• Is adherence likely to be similar?
• Are outcomes measured over a sufficient period?
• Do costs and care pathways transfer to the jurisdiction?
• Were important population groups excluded?

A tightly controlled trial can estimate an internally valid effect while requiring additional evidence for implementation or economic evaluation.

A simplified example

Suppose a clinical trial assigns 600 participants to a new rehabilitation programme or usual care. The programme improves a patient-reported function score at six months, but it requires additional therapist time and the difference narrows by 12 months.

The clinical analysis should estimate the effect at the prespecified time and report missing data and uncertainty. The economic analysis should include programme costs, other healthcare use and quality of life during follow-up.

A long-term model should not assume that the six-month improvement continues indefinitely. It should test alternative patterns of effect after the observed trial period and report how those assumptions change cost effectiveness.

Clinical trials and related study designs

Clinical trial is an umbrella concept that overlaps with several more specific designs. Clear terminology prevents the strengths of one design from being attributed to another.

• A randomised controlled trial is a clinical trial that uses random allocation to create comparison groups.
• A non-randomised trial prospectively assigns interventions without random allocation.
• An observational study does not assign the intervention under a research protocol.
• A pragmatic trial is designed to estimate effects under conditions closer to routine practice.
• A feasibility or pilot trial tests whether and how a larger study can be conducted.
• A single-arm trial evaluates an assigned intervention without a concurrent control group.

The evidence claim should reflect the specific design rather than the broad clinical-trial label.

Common misunderstandings

A clinical trial is not automatically randomised, blinded, controlled or confirmatory. These are separate features that must be described and evaluated.

Common misunderstandings include:

• Every clinical trial is not a randomised controlled trial.
• Prospective intervention assignment does not eliminate confounding when allocation is non-random.
• A statistically significant result is not automatically clinically important.
• Lack of statistical significance does not prove that interventions are equivalent.
• A biomarker change does not automatically demonstrate patient benefit.
• Trial registration does not guarantee valid conduct or complete reporting.
• Treatment discontinuation is not the same as withdrawal from follow-up.
• A successful early-phase trial does not establish routine-practice effectiveness.
• Trial evidence alone may not resolve long-term safety, affordability or implementation.

Interpreting clinical-trial evidence

Clinical-trial evidence should be interpreted through the question, allocation method, comparator, outcome, follow-up, analysis and applicability. The broad label does not determine the certainty or relevance of the result.

A useful trial makes its design and conduct transparent and matches its conclusion to the evidence it generated. Decision makers can then judge whether the observed benefits and harms justify further research, regulatory action, adoption or economic evaluation.