CDISC standards help streamline datasets (e.g. SDTM and ADaM), yet analysis results lack a common model. The emerging Analysis Results Standard (ARS) will help to fill this gap with machine-readable specifications. Mosaic couples ARS with an open-source stack to generate fully validated standard TFLs, fast and reproducibly.
How it works
Mosaic captures the Analysis Results Dataset (ARD) requirement in ARS-aligned YAML; updates and changes are made by editing this metadata, not code. After LinkML validation, a Python layer stores the YAML in a database via a SQL Alchemy ORM engine. The results-generation layer is intentionally language-agnostic . The rules that transform ADaM datasets into the ARD are expressed as metadata that can be rendered in any statistical language. Mosaic’s implementation uses R to derive the ARD.
A React UI reads the validated ARD. Here, users have controlled customisation options. Users can preview each TFL shell in real time and export regulator-ready RTF files suitable for submission packages. 
Mosaic replaces ad-hoc programming with a transparent, standards-based pipeline, accelerating TFL delivery while safeguarding traceability and regulatory compliance. This reallocates programmer effort from repetitive coding to high-value scientific review
Conor Moloney
Novartis
Mosaic: Open-Source, ARS-Driven Automation of Standard TFLs
CDISC standards help streamline datasets (e.g. SDTM and ADaM), yet analysis results lack a common model. The emerging Analysis Results Standard (ARS) will help to fill this gap with machine-readable specifications. Mosaic couples ARS with an open-source stack to generate fully validated standard TFLs, fast and reproducibly.  How it works  Mosaic captures the Analysis Results Dataset (ARD) requirement in ARS-aligned YAML; updates and changes are made by editing this metadata, not code. After LinkML validation, a Python layer stores the YAML in a database via a SQL Alchemy ORM engine. The results-generation layer is intentionally language-agnostic . The rules that transform ADaM datasets into the ARD are expressed as metadata that can be rendered in any statistical language. Mosaic’s implementation uses R to derive the ARD.  A React UI reads the validated ARD. Here, users have controlled customisation options. Users can preview each TFL shell in real time and export regulator-ready RTF files suitable for submission packages.   Mosaic replaces ad-hoc programming with a transparent, standards-based pipeline, accelerating TFL delivery while safeguarding traceability and regulatory compliance. This reallocates programmer effort from repetitive coding to high-value scientific review