From SDTM and ADaM to TFL: what automation changes

From code generation to a delivery system

SDTM, ADaM, and TFL work is often described as a programming sequence. In practice, each output depends on decisions about source interpretation, standard mappings, derivations, analysis populations, metadata, presentation rules, and validation. Generating code addresses only one part of that system.

The larger change comes when specifications, inputs, execution, validation results, review decisions, and formal outputs share one working state. A program then implements an approved specification while statistical intent remains connected across the specification, code, and result.

The delivery chain

For SDTM, the system applies approved mappings, terminology, variable rules, and conformance checks while assembling source and mapping questions for review. For ADaM, it executes declared derivations and population logic while retaining lineage back to SDTM and analysis decisions. For TFL, shells and output information remain connected to approved datasets, statistical methods, and display conventions.

Across all three stages, repeatable work enters one execution flow. Draft results receive structure, lineage, expected-form, and consistency checks, with the results assembled for focused review and complete statistical and programming context.

Professional roles and system support

Statisticians own estimands, analysis methods, and interpretation; programmers own specification implementation and program quality; data specialists own source and standards mapping; reviewers own independent checks and delivery confirmation. The system organizes inputs, execution, validation results, and review records around those responsibilities.

Exceptions, open questions, and check results enter one review record, where the appropriate professional role completes the decision and confirms the formal deliverable. Program execution and statistical accountability remain clearly connected throughout the workflow.

A different programming role

Programming shifts from manually carrying context between disconnected files toward designing specifications, reusable rules, validators, and exception paths. The programmer focuses more effort on making analysis intent explicit, reusable, and testable through structured specifications that connect requirements to implementation.

Statistical programming therefore expands its influence from individual scripts to the full delivery system. Code, metadata, validation, and review form a continuous path, so changes can be assessed against dependencies and rerun under consistent conditions.