Template

Pre-registration & Analysis Plan Template

A reproducible, guided pre-registration form that captures study aims, hypotheses, outcomes, sample size logic, analysis steps, handling of missing data, stopping rules, and data/code sharing plans — with fields designed to lock in decisions and reduce post-hoc bias.

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Pre-registration & Analysis Plan

Use this form to pre-register your study and lock in analysis plans before seeing outcomes. Completing this reduces bias, improves reproducibility, and makes your decisions auditable. Provide enough detail that another researcher could reproduce the analysis.

Describe the primary goals, rationale, target population, and what success looks like (2–4 sentences).
State the primary hypothesis clearly and in testable form (prefer explicit null and alternative where applicable).
List important secondary or exploratory hypotheses you plan to test or describe. Mark exploratory items clearly.
Define the exact variable(s), units, timing, and calculation. Example: 'Mean change in systolic BP (mmHg) at 12 weeks: average of two seated readings using calibrated device.'
Choose the general data type to guide analytic approach.
List pre-specified secondary outcomes with the same level of definition as the primary outcome.
Enter the total planned sample size.
Provide the calculation or paste code: assumed effect size, standard deviation or event rate, alpha, power, one-/two-sided test, and any adjustments (e.g., cluster design). Include formula or link to script/notebook.
Describe randomization method, stratification factors, block sizes, allocation ratio, or other assignment rules.
Specify who is blinded (participants, caregivers, outcome assessors) and how blinding is maintained.
Describe data sources, instruments, timing, quality checks, data entry, and who collects data. Note collection windows and calibration procedures if applicable.
Choose the main analysis set you will use for the primary outcome.
Describe the statistical models, transformations, covariates, estimation methods, and hypothesis tests you will use. Be precise (e.g., 'linear regression of outcome on treatment and baseline outcome, robust SEs').
List pre-specified secondary analyses, subgroup analyses, and interaction tests. Distinguish confirmatory vs exploratory analyses.
State correction or control strategies (e.g., Bonferroni, False Discovery Rate, hierarchical testing) or rationale for not adjusting.
Describe analyses to check assumptions (e.g., alternative model specifications, different imputation models for missing data).
Define how missing data will be handled (e.g., multiple imputation details), and any pre-specified exclusion criteria (with objective rules).
Specify any planned interim looks, stopping criteria, alpha spending plan, and Data Safety Monitoring Board (DSMB) roles, if applicable.
Concrete criteria for declaring success, failure, or next steps. Avoid vague language such as 'statistically significant' without thresholds and measures defined.
Where and when will data and code be shared (repository, access conditions, embargo period, DOI). Include de-identification steps if sharing human data.
Provide repository URL (GitHub, OSF, Zenodo) or state 'will be uploaded after data collection.'
e.g., R 4.2.1, Python 3.9 with specific libraries and versions.
Select Yes if approval exists; if No, provide expected date in the next field.
Approval number or expected date.
e.g., ClinicalTrials.gov NCT..., OSF registration URL. If not registered, indicate the plan and expected timeline.
Paste important equations, tables, or links to power calculation files, analysis notebooks, or protocol PDFs.
Confirm to lock in this plan. Amendments should be documented with reason and timestamp.
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