How to Plan a Clinical Research Study: From Research Question to Publication

1. Start with a research question worth answering

Most research begins with an observation, an unanswered question or a problem encountered in clinical practice. That is a useful starting point, but it is not yet a research plan. Suppose a team wants to investigate exercise and hypertension. The broad topic, exercise and blood pressure in adults with hypertension, leaves several decisions unresolved: what type of exercise, compared with what, in which patients, measured how and over what period?

A more useful question is: In adults with hypertension, does a supervised exercise programme, compared with usual care, reduce systolic blood pressure after 12 weeks? This identifies the population, intervention, comparator, outcome and follow-up period. These are the main elements of PICO, with an added time component. Although this question does not guarantee a good study, it makes the necessary planning decisions easier to identify.

Decide what the study is intended to establish

Clinical research can estimate an intervention effect, investigate an association, evaluate diagnostic accuracy, develop a prediction model or describe the frequency of a condition. These objectives do not necessarily require the same design. A randomised trial comparing supervised exercise with usual care addresses a different question from an observational study examining whether people who exercise more frequently have lower blood pressure. However, in the observational study, age, medication use, other health behaviours, disease severity and additional factors could influence the association. It should not automatically be interpreted as the causal effect of prescribing exercise.

Distinguish confirmatory and exploratory research

Confirmatory research investigates a sufficiently defined question using a prespecified strategy for estimating or testing the primary effect. Exploratory research investigates patterns or generates hypotheses for further study. Both can be scientifically useful, but an unexpected exploratory finding should not be presented as though it was the original confirmatory objective. Define the primary question before finalising the design, and identify which additional questions are exploratory.

2. Review the existing evidence before designing the study

A research question can be interesting without being new, and new without being useful. Before investing in data collection, establish what previous research has shown, where its limitations lie and what the proposed study could contribute. For the exercise and blood-pressure example, previous trials may inform the intervention, comparison group, measurement procedures, expected variability and recruitment challenges.

A useful review asks what is currently known, which study designs and measurements have been used, how consistent the findings are, what important uncertainties remain and whether the proposed study could realistically address them. Some projects need a focused review for initial planning; others need a formal evidence synthesis. The appropriate depth depends on the question and the decisions the review must support. Record important sources and the reasoning behind design choices so the protocol’s background and rationale are traceable.

3. Choose a study design that can answer the question

The design determines which comparisons are possible, what sources of bias need consideration and which conclusions the data can support. There is no universally best design independent of the question.

Interventional versus observational studies

In an interventional study, researchers assign an intervention or strategy according to the protocol. Observational research investigates exposures, characteristics or outcomes without assigning the exposure being studied. A well-designed randomised controlled trial can provide strong evidence about an intervention effect. Random allocation helps prevent systematic baseline differences between groups, on average, but it does not eliminate measurement problems, missing data, departures from assigned interventions or every other source of bias.

Observational studies can be useful when randomisation is impractical, unethical or unnecessary for the research question. Nevertheless, non-randomised comparisons require attention to selection, confounding and the assumptions needed for causal interpretation.

Prospective versus retrospective studies

Prospective studies generally establish a plan and collect information as relevant events or measurements occur. Retrospective studies examine information about events that have already happened, often using existing records. These terms describe aspects of timing and data availability; they do not, by themselves, establish whether a causal conclusion is justified. A retrospective analysis may be efficient, but existing records may not contain all the variables needed to answer the question.

Choose a design that suits the comparison

For the exercise study, a 2-group parallel randomised trial could compare supervised exercise with usual care over 12 weeks. A crossover design would raise questions about whether intervention effects persist between study periods and whether a suitable return to baseline is feasible. A study of acute blood-pressure responses during a single laboratory exercise session would require a different structure. Choose the design that fits the scientific question, not simply the one the team has used before.

4. Define the study population and unit of analysis

A study recruits particular people, measures them under particular conditions and attempts to draw conclusions that may extend beyond those individuals. Identify the target population and distinguish it from the sample you can realistically recruit. The intended population may be adults with hypertension, while the recruited sample consists mainly of patients attending one outpatient clinic who can travel to supervised exercise sessions. Those populations are not necessarily interchangeable.

Eligibility criteria should reflect the question, participant safety and feasibility. For the exercise study, relevant considerations might include the definition of hypertension, medication use, contraindications to exercise and capacity to complete follow-up. The precise criteria require clinical and methodological justification.

Identify independent observations

A common statistical mistake is treating every measurement as an independent observation. If 40 participants provide blood-pressure measurements at 4 visits, the resulting 160 measurements are not 160 independent participants. Measurements from the same person are related. Similarly, repeated assays from one biological sample are not independent biological samples. The analysis must respect the data structure because dependence affects uncertainty, sample-size planning and model choice. See How to Choose the Right Statistical Test for Your Study for more detail.

5. Choose outcomes and decide how to measure them

An outcome is the characteristic measured to answer the research question. For the exercise trial, systolic blood pressure at 12 weeks could be the primary outcome, but that description still needs detail. Will the study use clinic, home or ambulatory blood pressure? How many readings will be taken, under what conditions and how will they be summarised? These choices affect what the outcome represents and how reliably it is measured.

Define the primary outcome precisely

The primary outcome should align with the question and be sufficiently specific for study planning and analysis. For a continuous outcome, define the measurement method, units and main timepoint. If the outcome is binary, specify what constitutes an event. With time-to-event outcomes, define both the event and the start of follow-up. Saying only that the study will measure cardiovascular health is too broad.

Choose secondary outcomes for a reason

Secondary outcomes may investigate additional effects, mechanisms, safety or clinically relevant dimensions of the question. However, measuring more outcomes does not automatically improve a study. Each brings work in collection, checking, analysis and interpretation, and may create additional statistical comparisons. Include them because they provide meaningful information, not because the laboratory equipment offers another export column.

Consider measurement quality

An appropriate statistical model cannot fully compensate for an outcome that is measured inconsistently or does not represent the intended construct. Consider validity, reliability, assessor training, standardisation, assessment timing and potential missing observations. For blood pressure, the measurement setting and procedure matter. Define these procedures before collection begins and document them so that another researcher can understand what was done.

6. Define the comparison and plan the statistical analysis

Statistical planning should begin while the study is being designed, not after the dataset has been assembled. First identify what the analysis is intended to estimate. In the exercise trial, the primary interest might be the difference in mean systolic blood pressure at 12 weeks between participants assigned to exercise and usual care. That differs from asking whether blood pressure decreased significantly within the exercise group. A significant within-group change does not, by itself, establish a greater intervention effect than in the comparator.

Define the effect or association of interest

Specify the population, comparison, outcome, timepoint and summary measure. In clinical trials, an estimand precisely defines the treatment effect corresponding to the question. ICH E9(R1) provides a framework that also considers events after treatment assignment, including discontinuation and additional treatment (ICH E9(R1), 2020). Not every academic study requires the same formal documentation as a regulated trial, but defining the intended comparison clearly is useful in almost every analytical study.

Choose the primary analysis strategy

For a parallel-group randomised trial with baseline and follow-up continuous outcomes, baseline-adjusted regression or analysis of covariance is often appropriate for estimating the between-group treatment effect. Vickers and Altman (2001) explain why baseline adjustment can offer advantages over an unadjusted comparison of change scores in randomised trials. The final model must still suit the outcome, target effect and data structure. Repeated measurements, clustering, binary outcomes and time-to-event outcomes may require different approaches.

Plan how complications will be handled

Before inspecting results, consider the analysis population, missing data, covariates, repeated or clustered observations, model assumptions, multiplicity, justified sensitivity analyses and the distinction between primary and exploratory analyses. In a trial, stopping the assigned intervention is not necessarily equivalent to having missing outcome data. Whether outcomes after discontinuation remain relevant depends on the treatment effect being estimated. Document the decisions in the protocol or a statistical analysis plan, as appropriate.

Prespecification helps reduce avoidable analytical discretion, but it cannot make an inappropriate analysis scientifically correct. If the plan changes, document what changed, why and when. Changes made without knowledge of the relevant outcome results are generally easier to distinguish from result-driven choices.

7. Check sample size, feasibility and resources

A scientifically worthwhile study is not necessarily feasible. The plan must account for how many participants or observations are needed, how they will be recruited and what resources are available.

Justify the sample size

The rationale should correspond to the primary objective and analysis. In a conventional superiority trial, it may use an expected difference, outcome variability, statistical power and a chosen type I error rate. Other studies may be better planned around estimation precision, event numbers, prediction-model requirements or other design-specific considerations. There is no universal sample-size formula, and assumptions should be supported by relevant evidence where possible.

Check recruitment and participant burden

If the exercise study needs 120 participants but one clinic can recruit only 2 eligible participants per month, the team may need additional sites, a revised design or different scope. Consider travel, study visits, intervention sessions, assessments and follow-up. More measurements may provide useful information but also impose demands on participants and staff.

Confirm access to equipment, facilities, personnel, clinical oversight, statistical expertise and funding. Agree on responsibilities with external laboratories or collaborators. Feasibility is part of scientific quality: a study that cannot collect the information required for its primary objective will struggle to answer the question.

8. Plan ethics, registration and responsibilities

Research involving human participants must address the applicable ethical and governance requirements before work begins. Procedures depend on the study, institution, country, sponsor and regulatory setting.

Ethical review and participant protection

The World Medical Association’s 2024 Declaration of Helsinki sets out ethical principles for medical research involving human participants, including research using identifiable human material or data. These include scientific justification, ethical review, participant welfare, informed consent, privacy and researcher responsibilities. For the exercise trial, planning could involve participant screening, intervention-related risks, clinical supervision, consent, confidentiality and adverse-event procedures. Researchers must follow the approvals and safeguards applicable to their study.

Study registration

Ethical approval and registration serve different purposes. Ethical review considers acceptability and oversight, while registration makes specified information publicly available and helps distinguish planned outcomes from those reported later. The ICMJE registration policy recommends that medical journal editors require public registration of clinical trials at or before first patient consent for enrolment as a condition of publication consideration. Its definition includes prospective assignment to health-related interventions, including behavioural treatments; the exercise trial could therefore fall within that definition despite not investigating a drug. Observational studies do not automatically fall under the same policy, although other registration or preregistration requirements may apply.

Does Good Clinical Practice apply?

ICH E6(R3) provides an international Good Clinical Practice framework for clinical trials within its scope. The European Medicines Agency lists the Principles and Annex 1 as effective from 23 July 2025; Annex 2 and the consolidated guideline are scheduled to take effect on 15 January 2027 (EMA ICH E6(R3) guidance). Establish the relevant institutional and regulatory requirements for each study rather than assuming that an academic observational project and a regulated investigational-product trial need identical procedures.

Agree on responsibilities and authorship

Identify who is responsible for recruitment, procedures, data management, analysis, oversight and reporting. Discuss authorship early, recognising that contributions may change. ICMJE authorship guidance recommends 4 cumulative criteria covering substantive contributions, manuscript drafting or critical review, final approval and accountability; it does not prescribe author order. Agreement on expectations can reduce disputes when the manuscript is nearly complete.

9. Write the protocol and supporting documents

Once the main scientific decisions are sufficiently developed, document the plan. A research protocol describes why the study is being conducted, what it aims to establish and how the research will be carried out. Depending on design, it may cover the rationale, objectives, population, procedures, interventions or exposures, outcomes, sample-size justification, analysis, data management and ethics.

Use appropriate protocol guidance

For randomised trials, SPIRIT 2025 (Chan et al. 2025) provides a checklist of 34 minimum protocol items. It improves transparency of protocol content; it is not a universal template for every clinical study or a guarantee that the underlying design is methodologically appropriate. Other research may require different reporting guidance, documentation or institutional templates.

Keep the supporting documents consistent

These documents may overlap but are not interchangeable. Check that the primary outcome, sample-size rationale and intended analysis agree across them. Record versions and amendments, and obtain approvals where required before implementing changes. A protocol should reflect the real history of the study, not be silently rewritten after the work has finished.

10. Prepare data collection before recruitment begins

Data quality begins before the first participant attends a study visit. Establish how information will be recorded, checked, stored and linked to participants or samples.

Create a data dictionary

A data dictionary defines variable names, measurement units, coding conventions, allowed values, timepoints and missing-data codes. A variable called SBP_12W may seem obvious to its creator, but another researcher still needs to know whether it represents the first reading, an average of several readings, a clinic measurement or an ambulatory measurement. The dictionary should answer that question.

Preserve source information

Maintain an appropriate record of original measurements or source data. Document corrections, exclusions, derived variables and transformations. An implausible blood-pressure value might indicate a transcription error, equipment problem or genuine observation; investigate it rather than automatically deleting it. Exact source-data and audit-trail requirements depend on the research setting.

Plan for missing data and quality checks

Where feasible, distinguish a missed visit, withdrawal and unusable measurement instead of recording them all as an unexplained blank. Useful checks include valid participant identifiers, dates, expected ranges, duplicates and consistency across data sources. A carefully managed spreadsheet may be suitable for some smaller academic studies if it satisfies the applicable requirements. Larger or regulated studies may need validated systems, formal access control and audit trails. The objective is reliable, traceable and appropriately protected data.

11. Conduct the study and document what happens

Once the study starts, follow the approved protocol and relevant procedures. Recruitment, adherence, measurements and follow-up may not proceed exactly as planned, so records should capture what actually happened.

Track participant flow

Document relevant stages such as eligibility assessment, enrolment, randomisation where applicable, allocation, follow-up and analysis. CONSORT 2025 (Hopewell et al. 2025) includes updated guidance on participant flow for randomised trials. A participant who stops an intervention is not necessarily lost to follow-up or excluded from analysis. Keep those events distinct and reconcile counts with the underlying outcome data.

The Study Flow Diagram Builder supports participant-flow documentation and count checks for relevant designs. For more detail, see the CONSORT 2025 flow-diagram guide and STROBE participant-flow guide.

Record changes and monitor quality

Document deviations, missed assessments, withdrawals and unexpected events that affect conduct or interpretation. Follow applicable approval and reporting requirements. Review data quality throughout collection instead of waiting until the final visit; early checks may reveal unclear fields, inconsistent procedures or unexpected missingness while a documented correction is still possible.

12. Analyse and interpret the findings

Once the data are ready, conduct the planned analyses and document justified departures from the prespecified strategy. Preserve the analysis dataset, code, software details and relevant decisions so the work can be checked or reproduced.

Report effect estimates and uncertainty

A P value does not describe the magnitude or clinical importance of an effect. For the exercise trial, readers need to know how much systolic blood pressure differed between groups, the uncertainty around that estimate and whether the result is clinically meaningful. Effect estimates and confidence intervals often communicate more than a statement of statistical significance. Precision also does not eliminate concerns about measurement quality, missing data, selection or other biases.

Distinguish planned and exploratory analyses

Separate the primary analysis from secondary and exploratory work. If analyses were developed after examining the results, report that distinction transparently. An exploratory finding may generate a useful hypothesis, but should not be presented as confirmation of a prospectively specified one.

Interpret the results in relation to the design

A randomised trial and an observational study can yield similar numerical estimates without supporting equivalent causal conclusions. For observational research, consider confounding, selection, measurement error and the assumptions needed for causal interpretation. For experimental research, consider randomisation, delivery, missing outcomes, adherence and analysis decisions. Conclusions should reflect what the study actually established, not simply what the researchers hoped to find.

13. Prepare the manuscript and publish the research

Thinking about reporting before data collection may prevent problems later. A reporting guideline can identify information worth documenting prospectively, including intervention details, outcome definitions, participant flow and analysis populations.

Choose the appropriate reporting guideline

CONSORT 2025 provides a 30-item checklist and participant-flow diagram for reporting randomised trials. STROBE provides recommendations for reporting cohort, case-control and cross-sectional observational studies. Other designs have different guidance. These checklists support transparent reporting. However, they do not substitute for appropriate design, conduct or analysis.

Build the manuscript around the findings

Once the analyses are sufficiently settled, decide which results belong in the main tables and figures. The Methods should explain what was done, the Results what the data show, and the Discussion what the findings mean in light of the question, existing evidence and limitations. Meanwhile, the Introduction should explain why the question required investigation. The abstract must accurately reflect the completed manuscript.

Before submission, check consistency among the abstract, main text, tables, figures, protocol, registry entry and supplementary material, and explain material deviations from the plan. A well-planned study can contribute useful evidence even when the results are uncertain, inconclusive or do not support the original hypothesis.

Before collecting data: a study-planning checklist

Use this checklist to identify important gaps before recruitment or data collection begins.

• The research question and primary objective are clearly defined.
• Existing evidence justifies the study.
• The chosen design can answer the question.
• Population and eligibility criteria are justified.
• The independent experimental or analysis unit is identified.
• Primary outcome, measurement method and timepoint are defined.
• The intended comparison or effect is clear.
• A primary analysis strategy has been planned.
• Missing data, dependence and relevant sensitivity analyses have been considered.
• The sample-size justification matches the objectives and design.
• Recruitment, staffing, equipment, time and funding are realistic.
• Ethics, consent, registration and governance requirements have been identified.
• Responsibilities and authorship expectations have been discussed.
• The protocol and relevant supporting documents are prepared.
• Data collection procedures and quality checks are ready.
• Relevant reporting guidance has been identified.
• The team can explain how the measurements and analysis address the question.

The checklist supports planning; it does not replace a study-specific protocol or applicable institutional and regulatory requirements.