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A strong statistical analysis plan defines how study data will be prepared, analysed, interpreted, and reported before the results are known.
The Methods Clinic supports clinicians, investigators, and research teams in developing clear and transparent statistical strategies for clinical trials, observational studies, pragmatic studies, registry-based research, and real-world data projects.
We work directly with your research objectives, study design, outcomes, estimands, data sources, and expected limitations. This helps ensure that each statistical method is scientifically appropriate, clearly justified, and capable of answering the intended research question.
Our support can begin during study design, protocol development, grant preparation, or before data analysis begins.
A protocol is more than an administrative document. It is the scientific and operational blueprint for the study.
Weak or inconsistent protocols can lead to unclear objectives, inappropriate outcomes, recruitment problems, incomplete data collection, analytical difficulties, ethics concerns, and challenges during funding or peer review.
The Methods Clinic helps investigators identify and correct these issues before study implementation.
We translate your research objectives into specific, answerable statistical questions.
We ensure that the proposed methods reflect the study design, outcome structure, sampling approach, and data-generating process.
We help document assumptions, definitions, exclusions, transformations, and analytical choices clearly.
We strengthen the connection between the statistical results, clinical question, uncertainty, and intended conclusions.
Methodological guidance across every analytical decision, helping you develop a clear, transparent, reproducible, and scientifically defensible statistical analysis plan.
Defining the primary and secondary analytical objectives, hypotheses, and decision-relevant comparisons.
Clarifying the target population, treatment or exposure condition, outcome, intercurrent events, and summary measure.
Defining intention-to-treat, per-protocol, safety, complete-case, modified, or other relevant analysis populations.
Clarifying primary, secondary, exploratory, safety, patient-reported, composite, and repeated outcomes.
Developing clear rules for variable construction, categorisation, transformation, derived variables, and coding.
Planning summaries of baseline characteristics, participant flow, exposures, outcomes, follow-up, and data completeness.
Selecting and justifying the principal statistical method used to answer the main research question.
Defining supporting, hypothesis-generating, subgroup, interaction, and supplementary analyses.
Determining which covariates should be included and whether adjustment is based on design, precision, confounding, or clinical relevance.
Planning methods to address measured confounding, including regression adjustment, matching, weighting, stratification, or other appropriate approaches.
Developing strategies for correlated observations, repeated assessments, trajectories, and within-participant change.
Planning survival, competing-risk, recurrent-event, censoring, and follow-up analyses where appropriate.
Accounting for participants nested within centres, hospitals, communities, clinicians, or other hierarchical structures.
Defining how missing outcomes, covariates, follow-up data, withdrawals, and incomplete observations will be assessed and handled.
Planning analyses that test the robustness of findings to alternative assumptions, definitions, populations, and methods.
Identifying clinically justified subgroups and specifying how treatment or exposure effect differences will be assessed.
Addressing multiple outcomes, comparisons, time points, subgroups, and the risk of false-positive findings.
Defining how statistical assumptions, model fit, influential observations, residuals, and other diagnostics will be evaluated.
Supporting plans for interim review, efficacy, safety, futility, or data-monitoring decisions where relevant.
Planning tables, figures, effect estimates, confidence intervals, uncertainty measures, and reporting formats.
Documenting software, packages, versioning, code review, validation, and reproducible analytical workflows.
Providing critical review of draft or near-final plans for clarity, consistency, completeness, and methodological strength.
Receive focused statistical and methodological guidance before your results influence analytical decisions.
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Developing a clinical study, preparing a grant application, planning an analysis, or refining a manuscript?
