SAS to R Migration Roadmap: The Five Phases, What Each Delivers and What It Costs

A phase-by-phase plan for moving clinical statistical programming from SAS to R: assessment, conversion, equivalence testing, documentation and sponsor/regulatory adoption. Deliverables, effort drivers and the mistakes that make migrations stall.

• A migration that works is a programme of five phases, each with a deliverable your QA group can file: Assessment → Conversion → Equivalence → Documentation → Adoption.
• Phase 1 should be fixed-scope and short (two to six weeks). It produces the inventory, risk classification and macro reuse map that let you price everything else on evidence.
• Effort is driven by the number of distinct macros, not the number of programs. Fifty studies built on twelve shared macros is a smaller job than ten studies with bespoke code.
• Start with a closed study so no live deliverable depends on the pilot, and keep the SAS originals as the reference until the equivalence report is signed.

Most SAS-to-R migrations do not fail on statistics. They stall because the scope was never bounded, because every programmer converted code their own way, or because “validation” was left for the end. The roadmap below is the structure we use to avoid all three. It maps directly onto what sponsors and CRO procurement teams ask for.

Phase 1: Assessment

Goal: know exactly what you have before deciding what to move.

Deliverables:

• Program inventory. Every SAS program, macro and format catalogue, with study, purpose, author, last run and line count. A script over the SAS logs and %INCLUDE tree does most of this.
• Complexity and risk classification. Programs scored on size, macro depth, external dependencies (e.g. PROC SQL against databases, custom formats) and output criticality. Usually three bands: straightforward, moderate, complex.
• Macro reuse map. Which macros are shared across studies and how often they are called. This is the single most important number in the project.
• Specification and shell review. Are there written specifications and mock shells for the outputs? Where they are missing, the SAS code is the spec, which raises the risk band.
• Migration candidate list. What should move first (shared, well-specified, high reuse), what should move later, and what should be retired or left in SAS because it is run once a year.

Typical effort: two to six weeks for a mid-sized CRO portfolio. This phase is fixed-price in our proposals because its scope is bounded by the inventory itself.

Phase 2: Conversion

Goal: a maintainable R codebase, not a line-by-line transliteration.

Deliverables:

• R programming standards. Short, enforced, with examples. Project layout, naming, how specifications are referenced, lint rules, and when code must be packaged.
• Package and environment strategy. Approved package list (pharmaverse first: admiral for ADaM, rtables/tern or gtsummary for tables, xportr for transport files), the renv lockfile policy, and the internal package repository if one is needed.
• Reusable functions and packages. Each shared macro becomes a documented, unit-tested function in an internal package. See turning SAS macros into R functions.
• Converted programs that call those functions to produce the datasets and TLFs for the pilot study.
• Formatting layer. RTF/DOCX/PDF output matching the sponsor’s shells, including titles, footnotes, pagination and page-by-group behaviour.

The effort driver is the macro map from Phase 1. Converting a macro once and reusing it is the entire economic argument for the project. Resist the temptation to convert programs study by study without this layer; it produces fifty copies of the same bug.

Phase 3: Equivalence and validation

Goal: documented proof that R reproduces the SAS reference.

Deliverables:

• Dual-programming protocol naming what is compared and how, with the numerical tolerance rules decided before the first comparison.
• Dataset-level comparison of every analysis dataset (structure, keys, values).
• TLF-level comparison of every displayed cell after a common rounding rule.
• Independent QC by a programmer who did not write the R code.
• Traceability from SAP to specification to program to output to QC record.
• Exception and deviation log with root causes and resolutions.
• Validation summary with counts: compared, identical, explained, deviations resolved.

Most differences that appear here are conventions: rounding, missing value handling, sums-of-squares types, tie handling. Each becomes a standard once documented.

Phase 4: Documentation

Goal: a document set your quality system and a sponsor’s auditor can accept.

Deliverables: the R validation plan, environment and package qualification records (riskmetric), SOPs for programming, QC and change control, the risk assessment, the traceability matrix, programmer and QC documentation templates, and a regulatory justification memo summarising why the R process meets expectations. Much of this is written in parallel with Phase 3, because Phase 3 generates the evidence it references.

Phase 5: Sponsor and regulatory adoption

Goal: the people who have to approve the change say yes.

Deliverables:

• Sponsor presentation of the approach, the evidence and the controls, pitched to QA and biostatistics heads rather than programmers.
• R adoption strategy for the organisation: which studies move when, training plan, how SAS and R coexist during transition.
• Regulatory positioning. A written response to “why R?” grounded in the FDA Statistical Software Clarifying Statement, the R Consortium pilot submissions, and your own validation evidence. Read Is R accepted by the FDA?.
• Response strategy for health-authority or sponsor questions about R versus SAS, including who answers and with what evidence.
• Submission package strategy. How R code, lockfiles and logs are delivered in an eCTD structure, following the pilot submissions’ precedent.

Sequencing and timeline

A realistic first cycle for one pilot study: Phase 1 in weeks 1-4, Phases 2-3 in weeks 5-16, Phase 4 largely overlapping weeks 10-18, Phase 5 at weeks 18-20. Later studies reuse the package layer and documentation, so each subsequent study is a fraction of the first.

Mistakes that stall migrations

1. Converting programs instead of macros. The codebase grows and nothing gets easier.
2. Deciding tolerances after seeing differences. The equivalence report loses credibility.
3. Leaving validation to the end. The evidence is scattered and the plan is written backwards.
4. A pilot on a live deliverable. Deadline pressure wins; the migration is blamed.
5. No owner for package approval. Programmers install what they need and the lockfile becomes a liability.

How we run it

We offer Phase 1 as a fixed-scope, fixed-price assessment, then price Phases 2-5 from its findings. The equivalence report and the templates are yours whichever way you continue. Details and a scoping call are on the SAS to R migration page and the services for CROs and pharma.