SAS to R Migration for Clinical Teams

A risk-managed path from SAS to a validated R stack for CROs and pharma: parallel runs, double programming, a PROC-by-PROC equivalence map, pharmaverse tooling and team training.

SAS licences are a six-figure line item; R is free, open and now demonstrably submission-capable — the FDA has reviewed fully R-based submission packages through the R Consortium pilots, and the pharmaverse packages used for ADaM and TLFs are built in the open by Roche, GSK, J&J and others. The question for most clinical teams is no longer whether R can do the work, but how to migrate without breaking a validated process. That “how” is what this page is about.

Planning a migration?

We run scoped pilot migrations: one study, parallel-run against your SAS outputs, with a written equivalence report your QA team can file. Ask for a free 20-minute scoping call — get in touch →.

The method

A risk-managed migration path

  1. Pilot on a closed study. Reproduce an already-delivered TLF package in R while the SAS originals stand as the reference. No live deliverable depends on the pilot.
  2. Parallel run & double programming. Every table is produced twice — R vs the SAS original — and compared programmatically. Differences are triaged: rounding conventions, tie-handling in nonparametric tests, variance defaults (Type III vs Type I sums of squares) account for most of them, and each gets a documented resolution.
  3. Validation documentation, not validation folklore. “Validated” describes your process: version-pinned package sets ({renv}), session information captured per run, package risk assessment ({riskmetric}), and QC logs. We deliver the templates and the evidence trail your SOPs need.
  4. Training on your own studies. Your programmers learn on your data and your shells — not toy examples — so the capability stays in-house after we leave. See our free R courses for the self-serve layer.

The programme

The five-phase migration roadmap

Procurement teams and QA groups ask for the same structure, so we run every migration as five phases with a filed deliverable at the end of each. Phase 1 is fixed-scope and fixed-price; the rest are priced from its findings.

Phase What it answers Key deliverables
1. Assessment What do we have, and what should move first? Program inventory, complexity/risk classification, macro reuse map, specification and shell review, migration candidate list
2. Conversion How do we build it so it stays maintainable? R programming standards, package/environment strategy (renv, pharmaverse), reusable functions and internal packages, equivalent datasets and TLFs, formatting layer
3. Equivalence & validation Does R reproduce the SAS reference, provably? Dual programming, dataset- and TLF-level comparison, numerical tolerance rules, independent QC, traceability, deviation log, validation summary
4. Documentation Will QA and an auditor accept it? R validation plan, SOPs, risk assessment, traceability matrix, package/environment qualification, programmer/QC documentation, regulatory justification
5. Adoption Will the sponsor and the regulator say yes? Sponsor presentation, R adoption strategy, regulatory positioning, health-authority question response strategy, submission package strategy

Read the detailed version: SAS to R migration roadmap: the five phases. Supporting guides: dual-programming tolerance rules, why SAS and R round differently, missing values in SAS vs R, from SAS macros to R functions, PROC FREQ/MEANS/UNIVARIATE in R, writing an R validation plan, package qualification with riskmetric, traceability matrix and R vs SAS for clinical trials in 2026.

The dictionary

PROC ↔︎ R equivalence map

SAS R equivalent Notes
PROC FREQ janitor::tabyl(), gtsummary::tbl_summary(), chisq.test(), fisher.test() Row/column percents and tests in one table object
PROC MEANS / PROC UNIVARIATE dplyr::summarise(), rstatix::get_summary_stats() Grouped stats via group_by()
PROC TTEST t.test() Welch is the R default — match SAS with var.equal = TRUE
PROC GLM / PROC ANOVA lm(), aov(), car::Anova(type = 3) SAS Type III sums of squares need car + effect coding
PROC MIXED lme4::lmer(), nlme::lme(), mmrm::mmrm() {mmrm} reproduces the clinical MMRM with Kenward-Roger df
PROC LIFETEST survival::survfit(), survdiff() Kaplan-Meier and log-rank
PROC PHREG survival::coxph(ties = "efron") SAS default ties are Breslow — set explicitly when matching
PROC LOGISTIC glm(family = binomial), gtsummary::tbl_regression(exponentiate = TRUE) ORs with profile-likelihood CIs
PROC REPORT / PROC TABULATE rtables, gt, flextable Shell-faithful clinical layouts; export to RTF/DOCX
PROC TRANSPOSE tidyr::pivot_longer() / pivot_wider()
PROC SORT + DATA step dplyr::arrange(), mutate(), filter(), left_join() Joins replace MERGE with clearer semantics
PROC IMPORT / PROC EXPORT haven::read_sas(), readr, writexl haven reads .sas7bdat directly — no export step needed
SAS macros R functions + {purrr}, parameterised Quarto Testable functions replace macro text substitution

The traps in the Notes column — Welch vs pooled t, Type III sums of squares, Breslow vs Efron ties — are exactly where naive migrations “fail validation”. They are convention differences, not errors, and a parallel run surfaces every one of them.

See the trap live — in your browser

Real R via WebAssembly: the same t-test, two conventions. PROC TTEST’s pooled default vs R’s Welch default — edit and press Run Code:

Neither is “wrong” — but if the SAP says one and the code does the other, your parallel run fails. This is why the equivalence map exists.

Why now

The ecosystem has matured

  • admiral — ADaM derivations as documented, testable R functions (pharmaverse).
  • rtables & gtsummary — shell-faithful clinical tables, including RTF/DOCX delivery.
  • FDA R-based submissions — the pilots that settled the “is R accepted?” question.
  • TLF gallery — what the R outputs actually look like, next to their mock shells.

Start with one study

A pilot migration is a fixed-scope, fixed-quote engagement — and the equivalence report is yours to keep either way. Tell us about your stack →