Clinical migration workflow

SAS to R for Clinical Trial Programming

SAS2R.ai helps clinical programming teams begin translating deterministic SAS logic into readable R while preserving the review, QC, traceability, and validation work required by their processes.

Try the SAS to R converter

Example

Derive an analysis population flag

SAS

data adsl;
  set source.adsl;
  if trt01p ne '' and randfl = 'Y' then saffl = 'Y';
  else saffl = 'N';
run;

library(dplyr)

adsl <- source_adsl %>%
  mutate(
    saffl = if_else(!is.na(trt01p) & trt01p != '' & randfl == 'Y', 'Y', 'N')
  )

What to validate after conversion

Start with deterministic data preparation and derivation logic.
Plan parity checks before treating converted code as complete.
Document assumptions and residual risks for review.

Always validate population definitions, source snapshots, missing values, and subject-level results against the SAS output, protocol, and your standards.

Frequently asked questions

Can converted R code be used directly in a regulated workflow?

The generated code can accelerate drafting, but teams remain responsible for validation, review, QC, documentation, and the procedures required by their organization.

What is a practical first step in a SAS-to-R clinical migration?

Begin with a contained deterministic program, define parity checks in advance, and keep a written record of assumptions, differences, and review decisions.

CDISC datasets

SDTM Programming in R for SAS Teams

Explore practical SAS-to-R patterns for SDTM data preparation, then validate controlled terminology, date handling, metadata, and domain conformance against your study standards.