Practical guide · Life Sciences & Biostatistics
How to scope a statistical-programming work package
A useful statistical-programming work package defines the question, the available inputs, the expected outputs and who will review them. Agree these before choosing tools or estimating implementation. A bounded analysis, derivation or QC task can be commissioned independently of a new platform.
Leap Dynamics GmbHUpdated:
1. Define the question and intended use
Describe the study or research question and how the output will be used. Distinguish exploratory work from an analysis governed by an approved plan. Identify the statistical decision-maker, the programming contributor and the independent reviewer.
If a required method is outside the team’s applicable experience, identify that need during scoping. The programming language does not determine the suitability of an analysis method.
2. Establish the input contract
Use a controlled transfer route for study data. An initial enquiry can describe structures and requirements without containing patient records. For real-world data, agree how fitness, missingness, cohorts and potential bias will be assessed before interpreting results.
| Input | Question to resolve |
|---|---|
| Data and metadata | Which delivery, dictionary, standards and versions apply? |
| Specifications | Which population, variables, derivations, outputs and conventions are approved? |
| Environment | What access, packages, dependencies and transfer arrangements are available? |
| Open issues | Who resolves missing definitions, inconsistent data or changed requirements? |
3. Make delivery and review concrete
List each deliverable: for example a derived dataset, table/listing/figure, reproducible program, analysis report or QC record. State its format, naming, dependencies and expected review evidence.
For each output, assign a reviewer and acceptance criteria. Compare against specifications and investigate discrepancies. Where tolerances are appropriate, agree and justify them rather than introducing them after seeing the result.
- Record the input version and execution environment.
- Keep technical checks distinct from scientific interpretation.
- Document findings, decisions, corrections and the accepted version.
4. Plan changes and handover
A changed dataset or specification can affect both implementation and review. Agree how changes are assessed, who approves them and which checks must be repeated.
Handover should make another authorised person able to understand and rerun the work: code, configuration, dependency information, execution instructions, outputs and review records. Study decisions and accountable review remain with the designated owners. Formal validation or submission activities require their own agreed scope.