What This Article Covers: This deep dive examines TFL Designer — the CDISC COSA-approved, open-source platform available at tfldesigner.org — from first principles. We trace its origins in the industry-wide problem of manual TFL shell production, walk through the CDISC Analysis Results Standard (ARS) foundation it is built on, dissect its six core capability areas, and map the complete automation workflow from ARS metadata export through siera R-package code generation to final display rendering. We then give an honest assessment of where TFL Designer currently delivers measurable value for statistical programmers, what gaps must close before widespread adoption becomes realistic, and the structural and organisational challenges that stand in the way.
Every statistician and statistical programmer in the pharmaceutical industry lives with a version of the same problem. A clinical study reaches its analysis-ready moment. The Statistical Analysis Plan has been approved. ADaM datasets have passed QC. The study team is ready to generate hundreds of Tables, Figures, and Listings for the Clinical Study Report — and yet, weeks of effort remain before a single TFL appears.
That effort goes into TFL shell creation: the painstaking, Word-document-based process of designing mock-up outputs, writing titles and footnotes, annotating ADaM sources, specifying display logic, and handing finished shells to programmers who then manually hard-code titles, replicate structures, and rebuild specifications that already exist in the shell but cannot be machine-read. The shell is a visual artefact. It has no programmatic identity. Nothing downstream can consume it automatically.
The scale of this waste is not trivial. A large Phase III CSR might contain 400 to 600 individual TFLs. Large-molecule oncology programmes routinely exceed that. Shells for all of them need to be drafted, reviewed, revised after SAP amendments, renumbered, and reformatted — then manually re-transcribed into program code by the statistical programmer. A 2019 PharmaSUG paper described this cycle plainly: the programmer's primary role is to create and QC TFL, with specification residing in a combination of a SAP and a set of display shells that live in a Word document the statistician maintains by hand.
At the same time, the regulatory expectations around analysis documentation have been rising. The FDA's Study Data Technical Conformance Guide calls for Analysis Results Metadata in Define-XML. PMDA expects it. The CDISC 360 proof-of-concept project, completed in the early 2020s, demonstrated that end-to-end automation of study specification, data processing, and analysis was achievable using a metadata-driven approach — but only if tooling existed to operationalise the concept. The gap between what the standards imagined and what the industry actually did by hand was enormous.
TFL Designer was built to close that gap.
The concept behind TFL Designer traces directly to the CDISC 360 project. Bhavin Busa, who served on the CDISC 360 team and co-authored the 360 White Paper, recognised that metadata-driven automation would require a dedicated authoring environment — a place where study teams could design TFL shells and simultaneously generate machine-readable metadata without switching between tools or duplicating work.
The initiative was formalised as a CDISC Open Source Alliance (COSA) project, giving it institutional standing within the CDISC open-source ecosystem alongside other COSA-endorsed tools such as {sdtm.oak} and OpenStudyBuilder. CDISC hosted two community workshops in 2022 to gather user requirements, define the problem statement, and build the collaboration base needed for a tool that had to work for all without disrupting the current workflows.
The guiding principles adopted from those workshops were deliberately pragmatic:
This dual-track model — a free community tier and a paid enterprise tier — allowed the project to advance without either becoming purely academic or requiring sponsors to commit commercial investment before seeing value.
The GitHub repository at bhavinbusa/tfldesigner seeded the open-source prototype. By the time Community Version 1.0.0 was released in 2024, the platform had matured into a production-ready web application at tfldesigner.org, with enterprise delivery through Clymb Clinical and integration with the CDISC eTFL Portal that launched in October of that year.
Understanding TFL Designer requires first understanding the CDISC Analysis Results Standard (ARS), because TFL Designer is, in the most precise sense, an authoring and export tool for ARS metadata. Without ARS, TFL Designer would be just another shell-design utility. With ARS, it becomes the entry point to a complete metadata-driven automation pipeline.
CDISC published ARS v1.0 in April 2024 after years of development by the Analysis Results Standard team. The standard defines a Logical Data Model that describes the full context of any analysis result — not just what number appears in a cell, but everything needed to reproduce, trace, and reuse it.
The key components of the ARS Logical Data Model are:
| Component Definition | |
| Reporting Event | Top-level container grouping all analyses for a study or submission deliverable |
| Output | A single table, figure, or listing to be produced |
| Display | The visual representation of an Output, including layout and formatting specifications |
| Analysis | A specific statistical operation applied to a defined population |
| Analysis Set | The patient population used (e.g., Safety Analysis Set, SAFFL = 'Y') |
| Data Subset | A filter applied within the Analysis Set (e.g., treatment-emergent adverse events only) |
| Analysis Grouping | The grouping variables applied (e.g., treatment arm, severity grade) |
| Analysis Method | The statistical procedure: count, proportion, mean, Kaplan-Meier estimate, etc. |
| Operation | A discrete step within an Analysis Method |
| Analysis Results Data (ARD) | The structured, machine-readable dataset containing the computed results |
The ARS model is implemented using LinkML, an open-source schema development language that generates downstream artefacts including JSON Schema, YAML, RDF, and Python dataclasses. The official exchange format for ARS metadata is JSON, though an Excel representation is also supported for human readability during development.
The critical insight behind ARS — and what distinguishes it from earlier metadata attempts — is the shift it proposes in workflow sequencing. Traditional practice generates Analysis Results Metadata retrospectively: the TFL is programmed, reviewed, approved, and then an ARM Define-XML entry is written describing what was done. ARS proposes the opposite: generate the metadata prospectively, before programming begins, and use that metadata to drive the programming.
When prospective ARS metadata is complete, it contains enough information for a code generator to write analysis programs automatically. Every filter, population flag, grouping variable, and statistical method has been specified in a structured, machine-readable form. The programmer's role shifts from writing code from scratch to reviewing and validating auto-generated code against the ARS specification — a substantially different and more defensible position in a GxP environment.
For statistical programmers: ARS is to TFL programming what the ADaM Implementation Guide is to dataset programming. It defines the specification standard. TFL Designer is the tool that makes authoring ARS metadata practical rather than theoretical.
TFL Designer is a web-based Software as a Service (SaaS) application that serves as the authoring environment for ARS metadata and TFL shells. Its fundamental design goal is to make the creation of machine-readable TFL specifications as fast and intuitive as creating the equivalent Word document — while simultaneously generating the structured metadata that the Word document never could.
The platform operates through a browser interface, requiring no local installation. Users interact with a point-and-click environment to design display layouts, configure analysis metadata, and export both human-readable shells and machine-readable JSON or Excel metadata artefacts in a single workflow.
TFL Designer is available in two tiers, Community and Enterprise, structured to balance open access with enterprise-grade compliance requirements.
| Feature Area Community (tfldesigner.org) Enterprise | ||
| Access | Free, open access at tfldesigner.org | Licensed via Clymb Clinical |
| TFL Shell Design | Full shell creation, edit, delete | Full shell creation, edit, delete |
| Template Library | 50+ Safety & Efficacy templates, FDA/JPMA; community-sourced additions | All community templates + company-wide proprietary templates |
| CDISC Library Access | Via AAPI — SDTM, ADaM, Controlled Terminology | Via AAPI — SDTM, ADaM, Controlled Terminology |
| ARS Metadata Export | JSON and Excel (ARS v1.0 compliant) | JSON and Excel + ADM for display rendering |
| Shell Export | RTF and PDF with TOC and bookmarks | RTF and PDF with TOC and bookmarks |
| Version Control / Audit Trail | Not included | GxP-compliant; 21 CFR Part 11 compliant |
| Collaboration | Single user | Multi-user; review and approval workflows |
| Custom API / MDR Integration | Not included | Custom APIs to sponsor Metadata Repositories |
| Governance Workflows | Not included | Review, approval, and governance workflows |
The Community version at tfldesigner.org is the practical entry point for organisations exploring ARS implementation. It is also the tool used by the CDISC eTFL Portal team to generate the standardised artefacts published in that portal — a strong signal of its role as the reference implementation for ARS authoring.
At its most immediate layer, TFL Designer replaces the Word-document shell. Users build display layouts in a web-based editor that provides a structural view of the output — title rows, subtitle rows, header columns, body cells, footnote blocks — without requiring any knowledge of word-processing formatting.
Shell creation can begin from scratch or from a template. The platform maintains a Table of Contents that auto-populates as outputs are added, and a global push mechanism allows changes to title formats, footnote conventions, or column structures to propagate across selected shells simultaneously rather than requiring output-by-output editing. This addresses one of the most time-consuming aspects of traditional shell management: the late-SAP amendment that requires reformatting dozens of outputs.
Users can define straddle columns, set repeating table structures, and configure figures and listings alongside tabular outputs. The Plotly Python library is integrated for figure mock-up generation, allowing graphical display shells to be visualised in the same environment as tabular ones.
TFL Designer connects directly to the CDISC Library through the CDISC AAPI (Automated API), giving users access to current SDTM and ADaM variable definitions and Controlled Terminology from within the application. This means that when a programmer populates the ARS metadata for an analysis — specifying which ADaM dataset and variable supplies a given result — the platform can validate that reference against the published standard in real time.
The AAPI integration keeps the tool aligned with CDISC Library updates without requiring manual synchronisation. As new versions of ADaMIG or SDTMIG are published, the library connection reflects them automatically.
This is TFL Designer's defining capability. For every output designed in the interface, the platform captures the ARS metadata components that describe it: the Analysis Sets (population flags and filter logic), Data Subsets (within-population filters), Analysis Groupings (treatment, visit, subgroup variables), Analysis Methods (the statistical operations to be performed), and Operations (individual computational steps within a method).
Multiple Statistic Sets can be configured — supporting scenarios where the same analysis is run under different population or filter definitions. The metadata is maintained in a structured internal model aligned to the ARS v1.0 Logical Data Model, ensuring that what the user designs in the visual interface maps cleanly to what the standard specifies programmatically.
The platform also supports reading from and writing to ADaM define.xml files, and can import external ADaM metadata from Excel. An organisation with an existing define.xml for their ADaM datasets does not need to re-enter variable definitions — they can import the specification and build ARS metadata on top of it.
A single click exports the complete ARS metadata for the study in either JSON format (the official ARS representation) or Excel format (for human review and communication with non-technical stakeholders). The JSON output conforms to the ARS v1.0 model and is the artefact consumed by downstream automation tools, most directly the {siera} R package described in Section 7.
The platform simultaneously exports the Table of Contents and TFL shells in RTF or PDF format, with auto-generated bookmarks, hyperlinks between the TOC and individual shells, and linking of repeat tables to their unique counterparts. The statistical programmer receives both a reviewer-ready document and a machine-readable automation input from the same operation.
Version 1.0.0 introduced a template library containing over 50 Safety and Efficacy TFL templates, including Oncology-specific displays and outputs following FDA and JPMA style guidelines. Templates are organised by disease area, therapeutic area, and indication, allowing programmers to select a starting point that matches their submission context rather than building from a blank shell.
The Community sourcing model allows any user to publish their templates and metadata to the shared community library. This is a structural commitment to the network-effect philosophy that underpins open-source tools in pharma: each organisation's investment in standardising a display type benefits the whole industry. Over time, the community library is intended to become a cross-industry repository of standardised display patterns, reducing duplication of effort across sponsors, CROs, and regulatory agencies.
Beyond the ARS metadata that drives analysis computation, TFL Designer Enterprise exports Analysis Display Metadata (ADM) — a structured specification of the formatting and layout applied to the final rendered output. ADM describes column widths, pagination, decimal alignment, label positioning, and other display-layer properties in machine-readable form.
When an ARD (the computed results dataset produced by siera or another analysis engine) is combined with ADM, the resulting display can be rendered programmatically to produce submission-ready RTF or PDF outputs without manual formatting. The TFL Viewer, a companion product under development by Clymb Clinical, imports ARD in JSON format (aligned with Dataset-JSON) alongside ADM to render and manage approved outputs in a centralised review environment.
Understanding TFL Designer as an isolated shell tool misses its purpose. Its value emerges within a complete metadata-driven pipeline.
Step 1 — Template Selection and Shell Design The statistical programmer or statistician selects relevant templates from the eTFL Portal or the TFL Designer template library. Templates are customised for study-specific column headers, subgroups, titles, and footnotes. The Table of Contents is built automatically. Global title and footnote settings are applied. Output numbering is configured per company conventions.
Step 2 — ARS Metadata Population For each output, the ARS metadata fields are populated within the TFL Designer interface. Analysis Sets are defined with their filter logic (e.g., SAFFL = 'Y'). Data Subsets are configured (e.g., AEFL = 'Y' for adverse events). Analysis Groupings are specified (e.g., TRTA for treatment arm). Analysis Methods and Operations are selected or defined. ADaM dataset and variable references are validated against the CDISC Library via AAPI.
Step 3 — Export A single export action produces: (a) the ARS metadata JSON file for all outputs in the study; (b) the RTF/PDF shell document with TOC, bookmarks, and hyperlinks; and (c) the ADM file (Enterprise tier) encoding display-layer formatting specifications.
Step 4 — Automated Code Generation via {siera} The ARS JSON file is passed to the {siera} R package using its primary function readARS. Siera reads the metadata and generates one R script per output defined in the reporting event. Each R script contains all code necessary — analysis set filters, data subset logic, grouping variable definitions, method calls to the {cards} and {cardx} R packages — to produce an ARD for that output when run against the applicable ADaM datasets.
The generated scripts are inspectable and editable. A programmer can review the generated code, understand exactly what analysis the ARS metadata specified, and make study-specific amendments where needed. The ARD produced by each script follows a standardised structure: one result per row, with a primary key composed of analysisID, operationID, and grouping variables.
Step 5 — Display Rendering The ARDs (one per output) are combined with the ADM from TFL Designer to render final displays. Pharmaverse R packages — {rtables}, {Tplyr}, {gtsummary} — handle the transformation of structured ARD rows into formatted table output. The rendered displays are exported to RTF or PDF for submission. The TFL Viewer ingests ARD in Dataset-JSON format for centralised review, annotation, and approval tracking.
End-to-End Traceability: Every row in the ARD is traceable to the ARSanalysisIDandoperationIDthat generated it. Every ARS component is traceable to the TFL Designer metadata that authored it. Every TFL Designer template is traceable to the eTFL Portal or community library source. The result is an unbroken chain from display cell to ADaM variable to SDTM source — the kind of end-to-end traceability that regulators are increasingly expecting and that the CDISC 360 vision described as its ultimate goal.
{siera} R PackageSiera (available on CRAN and GitHub at clymbclinical/siera) is the ARS-to-code translation layer. Its readARS function accepts either JSON or Excel ARS metadata and generates fully functional R scripts via meta-programming. It uses the {cards} and {cardx} packages to handle the statistical method operations specified in the ARS model. Siera does not itself compute results — it produces inspectable R code that, when executed against ADaM datasets, computes the ARD. This transparency is deliberately designed for GxP auditability.
{atlas} R PackageAtlas is a companion code generator developed by Clymb Clinical that handles the display-rendering side — transforming ARDs into formatted submission-ready tables and figures. Where {siera} handles the analysis computation layer (ADaM → ARD), {atlas} handles the display rendering layer (ARD + ADM → TFL output). Together they constitute the full automation pipeline from ARS metadata to finished deliverable.
The eTFL Portal, launched by CDISC in October 2024 and housed in the CDISC Knowledge Base, is a reference repository of ARS-compliant packages. Each package contains a definition of an analysis concept, example display shells, ADaM metadata, ARS metadata artefacts, and Analysis Results Datasets. The initial release focused on safety analysis displays. Future iterations will extend to efficacy and therapeutic-area-specific templates.
The eTFL Portal was built using TFL Designer Community Version to generate its standardised shells and ARS metadata artefacts. This makes the Community version the de facto reference implementation for ARS authoring and gives tfldesigner.org a direct institutional endorsement from CDISC.
TFL Viewer is a companion platform under active development that serves as a centralised hub for TFL review, approval, and management. It allows study teams to move away from managing hundreds of static RTF files across shared drives toward a single application where outputs can be viewed, annotated, compared across versions, and progressed through review and approval workflows. Integration with Dataset-JSON-formatted ARDs is on the development roadmap, creating a path toward AI-assisted CSR authoring from machine-readable result data.
TFL Designer is not formally part of pharmaverse, but its downstream ecosystem components — {siera}, {atlas}, and the broader ARD-to-TFL pipeline — align with pharmaverse's end-to-end vision of R-based open-source tooling from SDTM through to TFLs. The pharmaverse TLG catalogue at pharmaverse.org lists packages including {rtables}, {Tplyr}, {gtsummary}, {tern}, {teal}, {clinify}, and {autoslider} that collectively handle the rendering layer that {siera} feeds into. A programmer working in a pharmaverse R environment can adopt TFL Designer as the upstream metadata authoring step and feed its output directly into the pharmaverse display generation stack.
An honest assessment of TFL Designer's current state requires separating what it does well from what it promises but has not yet fully delivered for the average statistical programming team.
The most immediate and measurable benefit is the reduction in time to produce TFL shells. The platform claims a 90% reduction in shell build time, and while that figure depends heavily on how much of a study's scope is covered by existing templates, the directional claim is credible. Starting from a template that already matches a therapeutic area's standard displays and pushing global title and footnote updates across all shells simultaneously is genuinely faster than maintaining a multi-hundred-page Word document.
For teams that have committed to ARS, TFL Designer is the only purpose-built authoring environment that makes capturing ARS metadata practically achievable rather than theoretically correct. Populating ARS JSON by hand is not a realistic option for a study with 400 outputs. TFL Designer provides the interface that makes ARS metadata generation part of the normal shell design process rather than a separate, parallel exercise.
The eTFL Portal's safety analysis packages — built with TFL Designer and available for free — give teams a validated starting point for common safety displays. A programmer can download an eTFL package, import the ARS metadata into TFL Designer, customise it for study-specific requirements, and export a study-ready shell set and ARS JSON in a fraction of the time required to build the same deliverables from scratch.
Beyond immediate efficiency gains, TFL Designer positions teams for the direction regulators are moving. FDA's increasing emphasis on machine-readable analysis metadata, PMDA's explicit expectations for ARD-equivalent deliverables, and the CDISC ARS standard's institutional backing all point toward a future where prospective metadata authoring is expected rather than optional. Teams that adopt TFL Designer now build the process knowledge and tooling infrastructure that will be required by the next generation of submission standards.
Despite genuine value, TFL Designer faces real barriers to becoming the default TFL authoring environment across the industry. These barriers are worth examining candidly.
TFL Designer's power is inseparable from ARS complexity. The standard is rigorous and precise — which is exactly what makes it useful for automation — but authoring ARS metadata requires understanding Analysis Sets, Data Subsets, Analysis Groupings, and Analysis Methods as distinct, composable concepts rather than as attributes of a display layout. Statisticians and programmers accustomed to writing SAP language and then translating it into code will find the ARS metadata model requires a significant change in how they think about analysis specification.
The tool needs substantially richer contextual guidance — inline explanations, worked examples, and error messages that explain not just what is invalid but why and how to fix it — to make the ARS learning curve manageable for users approaching it without prior standards exposure.
The current library of 50+ templates covers common safety displays and some oncology and efficacy patterns well. It does not yet cover the full breadth of analyses that appear in typical CSRs across diverse therapeutic areas: neuroscience, cardiovascular, rare disease, paediatric oncology, immunology, and ophthalmology each have display conventions that do not map cleanly to general safety templates. Until template coverage approaches the full scope of what programmers actually produce, significant custom shell work remains necessary, reducing the efficiency gain.
The community sourcing model is the long-term answer to this gap, but it depends on network effects that take time to accumulate. Tooling to make community contribution easier — guided template publication workflows, validation of contributed artefacts against ARS conformance rules — would accelerate this.
The downstream automation pipeline described in this article — {siera} generating R scripts, {atlas} rendering displays — is an R-native workflow. The majority of the pharmaceutical statistical programming community, particularly at large sponsors and CROs, still operates primarily or exclusively in SAS. TFL Designer can produce ARS JSON that theoretically could drive a SAS-based code generator, but no equivalent of {siera} exists for SAS today.
Until a SAS automation engine that consumes ARS metadata is available, the end-to-end automation value of TFL Designer is inaccessible to SAS-first organisations. They receive better shells and exportable metadata, but they cannot complete the downstream automation that makes the investment in ARS authoring pay off at scale. Developing a SAS-compatible ARS ingestion layer — whether as an open-source macro library or a commercial product — is the single most important step toward making TFL Designer relevant to the broadest segment of the programming community.
For GxP use in a regulated submission environment, the Community version's lack of version control, audit trail, and user access management means that outputs from it cannot be directly used in a validated submission workflow without supplementary controls applied outside the tool. Organisations that pilot TFL Designer in the Community version and wish to move it into production for submissions must either adopt the Enterprise tier or build compensating controls around the Community version — both of which carry cost and process overhead.
Clearer validation documentation, IQ/OQ/PQ templates, and explicit regulatory guidance on how TFL Designer outputs relate to Define-XML and submission artefacts would reduce the friction of regulatory review and system validation activities.
Most large sponsors and CROs have invested in Metadata Repository systems, define.xml tooling, and internal specification management applications. TFL Designer's custom API capability (Enterprise) allows integration with external MDRs, but building and maintaining those integrations requires technical investment that is organisation-specific. Standardised integration connectors or published API schemas that work out of the box with common industry MDR platforms would lower this barrier substantially.
The ARS standard itself, the eTFL Portal templates, and the {siera} package are all in early versions. The eTFL Portal's initial focus on safety analyses reflects a logical prioritisation but leaves efficacy analyses — survival analysis, mixed-effects models, dose-response modelling, patient-reported outcome analyses — under-specified in the available templates and under-tested in the siera code generation pipeline. Complex statistical methods that go beyond descriptive summaries or basic inferential statistics require ARS Methods and Operations definitions that have not yet been worked through in detail in the published examples.
One integration that does not belong on the near-term improvements list but deserves explicit placement on the strategic roadmap is a bridge between TFL Designer and the CDISC Unified Study Definitions Model (USDM) as operationalised through TransCelerate's Digital Data Flow (DDF) initiative. The conceptual fit is strong enough that ignoring it would be an oversight; the practical readiness is not there yet, which is why it sits here rather than alongside the operational gaps described above.
The CDISC end-to-end standards vision — articulated through the CDISC 360 project — describes an unbroken chain from protocol concept through data collection (CDASH), tabulation (SDTM), analysis (ADaM), and reporting (ARS/TFL). USDM is the protocol concept layer: it defines study arms, epochs, estimands, primary and secondary endpoints, intercurrent event handling strategies, and the population definitions that underpin every downstream analysis decision.
ARS is the analysis specification layer sitting immediately downstream: it defines Analysis Sets (populations), Data Subsets (filters), Analysis Groupings (stratification variables), and Analysis Methods (the statistical operations). The structural relationship between a USDM estimand and an ARS Analysis is not coincidental — both are trying to describe the same clinical question, one at protocol design time and one at analysis specification time.
| USDM Estimand Component Corresponding ARS Component | |
| Population definition | Analysis Set (e.g., SAFFL = 'Y') |
| Variable of interest | Analysis Method + Operation |
| Intercurrent event handling | Data Subset filter logic |
| Summary measure | Analysis Method type |
| Treatment arms | Analysis Grouping values |
| Visit schedule | Analysis Grouping (by-visit) |
If TFL Designer could ingest a USDM JSON document, it could offer scaffolding: treatment arm labels from the protocol arms pre-populating Analysis Grouping values; the Safety Analysis Set population flag seeded from the USDM population definition; by-visit structures inferred from the visit schedule; and primary endpoint analysis shells seeded from estimand definitions. The statistician would confirm, refine, and extend — but not start from a blank specification.
Three conditions must hold before this integration pays off, and none of them are fully met today.
First, USDM/DDF adoption must reach a threshold where a meaningful fraction of incoming studies actually have machine-readable USDM documents available at TFL design time. That threshold has not been reached. Most sponsors are still in pilot or evaluation phases with DDF. A bridge to a standard that most organisations do not yet produce provides limited marginal value.
Second, the mapping from USDM to ARS is not mechanical. A USDM estimand expressed in structured natural language does not automatically resolve to a specific ADaM population flag. The intercurrent event strategy "hypothetical" does not automatically resolve to a censoring rule for a Kaplan-Meier analysis. A human specification step remains necessary between USDM input and fully-formed ARS output. The integration would provide useful scaffolding — fields pre-populated with USDM-derived suggestions that the statistician then confirms or modifies — but it would be incorrect to describe it as a hands-free handoff.
Third, TFL Designer itself needs to finish closing its near-term gaps — SAS automation, GxP validation documentation, template library breadth — before the incremental value of a USDM bridge becomes the binding constraint on adoption. Building a sophisticated upstream integration before the downstream pipeline is mature enough for broad use inverts the priority order.
The USDM/DDF integration becomes a high-value investment once three things are in place: USDM adoption reaches a point where sponsors routinely produce machine-readable protocol definitions; the ARS-to-TFL pipeline is sufficiently mature and broad (covering complex efficacy methods, not just safety summaries) that the upstream link becomes the efficiency bottleneck; and someone — most likely a CDISC COSA project or a TransCelerate/CDISC collaborative initiative — does the specification work to define the mapping rules between USDM estimand components and ARS model components in a way the community agrees on.
That last point deserves emphasis. The mapping itself needs to be standardised before tooling implements it. If each tool vendor defines its own USDM-to-ARS translation logic, the industry ends up with incompatible interpretations of the same upstream document — exactly the fragmentation problem that both standards were designed to prevent. A published, community-agreed USDM-to-ARS mapping specification is the prerequisite that makes any implementation — including one in TFL Designer — interoperable and regulatorily defensible.
For statistical programmers following both standards: The most productive engagement right now is not waiting for tools to implement USDM/ARS bridges but contributing to the specification work that makes those bridges well-defined. CDISC working groups and COSA projects on both the USDM and ARS sides are the venues where that mapping work needs to happen. Tool implementation follows specification maturity, not the other way around.
Some of the barriers to TFL Designer adoption are not about the tool itself but about the industry context it operates within.
TFL shells in most organisations are owned by statisticians and reviewed by medical writers, regulatory affairs staff, and clinical team members who do not have programming backgrounds and are not comfortable with the concept of a web application as the specification document. The Word document TFL shell persists partly because of organisational muscle memory and partly because it is universally accessible — everyone with Microsoft Office can open, comment on, and approve it. Migrating that approval process to a web application requires change management that goes well beyond installing new software.
In many organisations, the statistician owns the SAP and the shell, while the statistical programmer owns the code. The ARS model blurs this boundary productively — by making the specification machine-readable, it eliminates the translation step between statistician intent and programmer implementation. But it also requires that statisticians become comfortable populating ARS metadata fields, which many will experience as a programming-adjacent activity they did not sign up for. Resolving who is responsible for authoring ARS metadata, and with what level of statistical programming knowledge, is an organisational question that tools alone cannot answer.
FDA has called for Analysis Results Metadata in submissions through the TRC guidance and the Study Data Technical Conformance Guide. But the specific format, granularity, and mandatory versus recommended nature of ARS-based ARD submission remain areas of evolving guidance. Sponsors and CROs making investment decisions about TFL Designer adoption in submission workflows are doing so against a backdrop of regulatory expectation that has not yet fully crystallised. This uncertainty is rational grounds for caution and slows adoption in risk-averse regulatory affairs and IT governance teams.
For statistical programmers and their managers, TFL Designer sits at an awkward adoption moment. ARS is new enough that most organisations have not yet committed to it. {siera} is in early versions. The eTFL Portal has initial safety packages but is not comprehensive. The end-to-end automation pipeline works in proof-of-concept presentations and case studies but has not been battle-tested across the full diversity of Phase III oncology or rare disease submission packages. Waiting for more maturity is rational. But the organisations that invest now will have process expertise and validated tooling when the regulatory expectation for ARS-based submissions hardens — and that expertise gap will be significant.
The most accessible entry point is tfldesigner.org. Registration is free. The CDISC eTFL Portal (accessible through the CDISC Knowledge Base) provides ready-to-use ARS packages for common safety displays. A reasonable starting exercise is to take an eTFL package for a familiar display type — a demographics table or an adverse events summary — import the ARS metadata into TFL Designer, and trace through the ARS components to understand how the standard describes the display you know how to program.
The complete pipeline is available as open-source today. Install {siera} from CRAN. Obtain an ARS JSON file from TFL Designer for a simple study. Run readARS and examine the generated R scripts. Run those scripts against ADaM datasets and inspect the ARD structure. This hands-on exercise builds concrete intuition for what ARS metadata drives and how quickly a code generator can operationalise it.
The immediate value is in shell production and metadata capture. Use TFL Designer to produce TFL shells faster and in better structural alignment with company standards. Capture ARS metadata even if no SAS consumption pathway exists yet. The metadata will not be wasted — when a SAS ARS automation layer becomes available, the metadata authored today is the investment that pays off. In the interim, exporting ARS metadata to Excel gives programmers a machine-readable specification that can be consumed by existing SAS macro frameworks even without a formal ARS ingestion engine.
TFL Designer is an open-source community project. Publishing study-specific templates to the community library, reporting bugs and capability gaps through GitHub, and participating in CDISC COSA discussions and PHUSE/PharmaSUG paper contributions accelerates the tool's development in directions that matter to practitioners. The tool was explicitly built to reflect industry workflow needs — that feedback loop only works if programmers contribute to it.
TFL Designer represents the industry's first serious attempt to operationalise the CDISC Analysis Results Standard as a practical authoring environment for statistical programmers and statisticians. It does this not by wrapping an existing workflow in new technology but by redefining what the TFL specification artefact is: not a Word document, but a machine-readable metadata structure that simultaneously produces human-readable shells and automation-ready JSON.
In its current form, it delivers genuine and measurable value in shell production speed, ARS metadata capture, eTFL Portal template utilisation, and positioning organisations for the direction regulatory expectations are moving. The end-to-end pipeline with {siera} and {atlas} demonstrates that automation from ARS metadata to final TFL display is achievable today, not hypothetically.
The barriers to widespread adoption are real and should not be minimised: the R-only automation pipeline excludes the SAS majority, the ARS learning curve is steep, the template library is incomplete, GxP validation overhead is non-trivial, and the regulatory expectation landscape has not fully crystallised. These are solvable problems — most of them are engineering, documentation, and community contribution problems rather than fundamental design flaws — but they will require sustained investment from both Clymb Clinical and the broader CDISC open-source community.
For statistical programmers, the practical calculus is this: TFL Designer at tfldesigner.org costs nothing to explore and requires no installation. The floor for engagement is low. The ceiling — a fully metadata-driven, end-to-end automated TFL pipeline that produces regulatory-traceable results with dramatically reduced manual programming effort — is the direction the industry is moving. Building familiarity with the tool and the ARS standard it implements is not optional preparation for the future of the discipline. It is the future, arriving in stages.
Consistent with clinstandard.org editorial standards, all factual claims are sourced from official CDISC documentation, peer-reviewed conference proceedings, or primary tool documentation.
{siera} R Package. CRAN: https://cran.r-project.org/web/packages/siera/index.html. GitHub: https://github.com/clymbclinical/sieraNo comments yet. Be the first!