SAS to R Converter for Clinical Trial Programming

Start by converting deterministic data preparation steps (joins, filters, derivations) and then apply a QC checklist (counts, keys, missingness, summaries). For SDTM/ADaM, validate controlled terminology, metadata, and derivations against your standards/SOPs.

Yes—SAS2R.ai is designed to translate common clinical trial programming patterns (DATA steps, PROC SQL-style joins, filtering, derivations) into readable R code.

Typically yes. Many PROC SQL patterns map cleanly to dplyr joins (left_join/inner_join) or data.table merges. Always validate row counts and key uniqueness, especially for one-to-many joins.

Most PROC SQL aggregations translate to dplyr group_by() + summarise(). Pay attention to missing values and whether SAS is implicitly converting types (e.g., character to numeric) in your source code.

Common BY-group patterns can be expressed with group_by()/arrange() and dplyr verbs, and lag/lead equivalents. Stateful RETAIN-style logic may require careful review to ensure the R translation matches SAS row-order semantics.

In R, you typically use explicit joins with keys (e.g., dplyr left_join). For SAS MERGE semantics, verify sort order/keys and decide how to handle duplicates; then validate the join cardinality and record counts after the merge.

When formats/informats are explicit, the output may translate them into factor labels, recode() maps, or parsing rules (e.g., dates). If your logic relies on custom formats, you may need to recreate the mapping in R for full fidelity.

Tabulation and reporting logic can often be translated into tidyr/dplyr summaries, gt, flextable, or rmarkdown outputs. Complex ODS layout styling is usually best handled by rebuilding the report formatting in native R tooling.

A common approach is dplyr/tidyr for data prep plus gt or flextable for tables/listings, and ggplot2 for figures. For production, teams often render outputs via Quarto/R Markdown and keep formatting rules version-controlled.

The converter is designed with a CDISC-aware mindset and common SDTM/ADaM derivation patterns in mind. You should still validate derivations, controlled terminology mappings, and dataset-level metadata per your standards and SOPs.

Yes for many core patterns (mapping raw to SDTM variables, controlled terminology mapping, standardizing dates/times). You should still validate against your Define-XML/metadata and confirm domain-level rules (e.g., required variables, sorting) match expectations.

It can accelerate common derivations (population flags, treatment dates, baseline/analysis windows) by translating deterministic logic. You should verify timing rules, windowing, and any sponsor-specific conventions through QC and review.

Simple macro variables and straightforward macro control flow can often be mapped to R parameters and functions. Large macro libraries may need a staged approach: convert core data steps first, then refactor macros into reusable R functions.

In R, macro variables are typically ordinary variables or function arguments. For repeatable pipelines, wrap logic in functions and pass parameters explicitly—this improves testability and reduces hidden state.

R uses NA for missingness. If your SAS logic uses special missing values (.A-.Z) to encode reasons, you may need to preserve that explicitly (e.g., an additional flag variable) to keep downstream behavior identical.

Often it can match closely, but exact parity depends on data types, sorting, missingness rules, and edge-case logic. Treat the output as a strong starting point and run row-level QC (counts, keys, summaries) before using in production.

It can accelerate development, but you remain responsible for verification and validation. Many teams use the output to speed up drafting, then apply standard code review, unit tests, QC checks, and documentation per SOP.

A practical approach is to standardize an R project template (packages, style, test strategy), define QC checklists for SAS↔R parity, and use code review gates so conversions are consistent across contributors.

It generally favors readable, maintainable idioms (often tidyverse-style), but the exact packages depend on your input code. You can align your team on preferred packages (e.g., dplyr vs data.table) and refactor accordingly.

The output is generated with Shiny-readiness in mind: modular, explicit transformations, and clear separation of data preparation and presentation logic.

Yes. A common workflow is to keep data prep in pure R functions (testable) and then call those functions inside Shiny server logic. This keeps your app maintainable and easier to validate.

We follow a privacy-first approach and aim to minimize retention. See the Terms for details about processing and storage behavior.

Start with deterministic checks: row counts, key uniqueness, variable types, and missingness. Then compare summaries and spot-check subject-level records. For production, add automated tests and keep conversion notes for traceability.