Plan: Statistical Programming and TLF Validation Plan (Per-Study)

This is a ready-to-use, per-study plan for validating a clinical trial’s statistical programming package: analysis datasets and every table, listing, and figure. It operationalizes the independent statistical programming and TLF validation SOP for one specific study, naming the real outputs, the real people, and the real dates, rather than restating the general procedure. Replace every <<FILL: ...>> placeholder, approve it before production programming begins, and amend it under change control if the output set or assignments change. A worked filled specimen follows. This is general educational content to adapt to your organization and study, not regulatory advice.

Why this plan earns its place

The SOP states how any study validates its programming: risk tiers, independent programming, compares, log review, sign-off. It does not, and should not, name a specific study’s actual output list, the specific people assigned to each tier, or the dates that lock the plan before unblinding. Without a study-specific plan, “we followed the SOP” is an assertion with nothing dated behind it: tier assignments can quietly happen after a difference is found, assignments can drift under deadline pressure with no record of who was supposed to do what, and there is no single, approved statement of what this study’s validation commitment actually was before programming started. This plan is that statement. Read together with the discrepancy log and program log review checklist, which record what happened during validation, and the TLF package QC sign-off record, which gates the finished package, this plan is the front end: what was committed to, for this study, before anyone wrote a line of code.

Document control

1. Scope

This plan covers the ADaM analysis datasets and the tables, listings, and figures for <<FILL: study ID>>, for <<FILL: analysis type: interim / final CSR / resubmission>>. It does not cover SDTM creation, governed by <<FILL: SOP-ID>>, or the SAP itself, governed by <<FILL: SOP-ID>>; this plan assumes both are already approved and version-controlled when production programming begins.

2. Approach

Summarize how this study will be validated: full independent double programming for Tier 1 and Tier 2 outputs with an automated compare, documented QC for Tier 3, program log review on every output in every tier, and traceability sampling on the primary and key secondary outputs, per the governing SOP. Note any study-specific exception (<<FILL: e.g. a vendor-supplied PK dataset validated under a separate protocol, excluded here and cross-referenced>>) and the reason for it.

3. Risk basis and output-tier assignment

Assign every planned output a tier before production starts. This table is the single most load-bearing part of the plan: the SOP sets the method, this table says which real output gets which method.

Extend this table to cover every planned dataset, table, listing, and figure, or reference the complete list in a controlled appendix if the package is large. A tier assigned after a discrepancy is found is not a valid entry; the governing SOP requires the tier set before production.

4. Roles and named assignments

No one appears as both production and validation programmer for the same output. Where staffing is thin enough that the same two or three people rotate across many outputs, confirm in writing that no individual validates their own work on any single output.

5. Schedule

A schedule that has validation finishing after unblinding for an output that depends on treatment assignment is a scheduling defect to fix now, not a finding to explain later.

6. Acceptance criteria

This plan is satisfied when every output listed in section 3 has a closed, signed validation record per the governing SOP; every tier assignment was made before that output’s production programming started; named assignments in section 4 show no one validating their own production work; the schedule in section 5 held, or deviations are documented and risk-assessed; and the completed package passes the TLF package QC sign-off.

7. References

The governing SOP, <<FILL: SOP-ID>> (independent statistical programming and TLF validation). ICH E6(R2), Good Clinical Practice; ICH E9, Statistical Principles for Clinical Trials. FDA Study Data Technical Conformance Guide.

Confirm the current version of each reference before issue.

8. Revision history

9. Approvals

Filled specimen (excerpt)

Illustrative plan for a phase 3 trial’s final CSR package.

Tier assignment (excerpt):

Schedule (excerpt): production programming 24-Feb to 30-Apr-2026; independent validation 01-Mar to 15-May-2026; database lock 10-Mar-2026; unblinding 11-Mar-2026 (validation of Tier 1 outputs completed and signed before unblinding where the output depends on treatment assignment); package QC sign-off 20-May-2026.

In this example, the tier table makes the staffing constraint visible before anyone starts. With three programmers covering a full Tier 1 and Tier 2 set, the plan’s own role table (section 4) is the check that no one ends up validating their own Table 14.3.2 or 14.4.5, because the assignments rotate across the three names rather than pairing the same two people on every output.

Common inspection findings this plan prevents

• Tier assignments that exist only in someone’s memory or a chat thread, with no dated, approved record of what was committed to before production started.
• A validation schedule that, in practice, let Tier 1 outputs reach unblinding before their independent validation closed.
• The same two programmers paired on every output in a small team, discovered only when an auditor asks who validated whom.
• A plan that lists tiers but no named people, so “who validated this” takes a search instead of a lookup.
• No single document distinguishing this study’s actual validation commitment from the SOP’s general method, leaving “we followed the SOP” unverifiable.

How to adapt this plan

1. Build section 3 from your real TLF shell inventory and ADaM specification once both are approved, not from a draft list that will still change.
2. Staff section 4 so that, across the whole tier table, no individual validates their own production output on any single line.
3. Set section 5’s unblinding milestone after every output whose validation depends on being read by treatment arm, not just the primary endpoint.
4. Route any change to the output list, tiers, or assignments after approval through a documented plan amendment, the same way a SAP change goes through an amendment.
5. Confirm every reference in section 7 against the current published version before issue.