SAS CLINICAL Professional

Explanation of category of domains

DISC Compliance refers to adhering to the standards set by the Clinical Data Interchange Standards Consortium (CDISC) for organizing, structuring, and submitting clinical trial data. These standards ensure that data is consistent, reliable, and ready for regulatory submission (e.g., to FDA or PMDA).

Variable Roles & Controlled Terminology – Description Only

In clinical data standards like CDISC (e.g., SDTM and ADaM), each variable has a specific role and must follow a controlled terminology to ensure clarity, consistency, and regulatory compliance.

In SDTM (Study Data Tabulation Model), the relational data structure refers to how datasets are organized and linked based on common identifiers, ensuring data traceability, consistency, and integration across subject-level observations.

Traceability & Regulatory Readiness – Description Only

Traceability and Regulatory Readiness are essential principles in clinical data standards like CDISC (SDTM & ADaM), ensuring that submitted data is transparent, reproducible, and review-ready for regulatory agencies

Validation in SDTM:

Validation refers to the process of checking whether the SDTM datasets conform to CDISC standards and regulatory expectations. It ensures that:

• The structure, variables, and values meet SDTM IG (Implementation Guide) specifications.

• Data is complete, logical, and consistent across domains.

• All datasets are compliant with controlled terminology, formats, and naming conventions

Supports Global Submissions – Description in SDTM Context:

Supporting global submissions means that the SDTM datasets are designed, structured, and validated in a way that meets the regulatory requirements of multiple health authorities worldwide, including:

• FDA (USA)

• PMDA (Japan)

• EMA (Europe)

• Health Canada

• NMPA (China) and others

To support global submissions, SDTM datasets must:

• Follow CDISC standards (SDTM/SDTM IG, Controlled Terminology).

• Be language-neutral and based on standardized definitions.

• Provide complete, traceable, and well-documented data.

• Ensure consistency and reproducibility of results across regions.

• Include standard metadata (Define-XML, SDRG) to aid reviewers in understanding the datasets.

Integration with other CDISC standards means that SDTM datasets work seamlessly with other components of the CDISC data lifecycle, ensuring a smooth flow of clinical data from collection to analysis and submission.

ADaM is a CDISC standard that defines how to structure analysis datasets to support statistical analyses in clinical trials. It ensures that the datasets are:

• Analysis-ready (designed specifically for statistical procedures)

• Traceable back to SDTM and raw data

• Standardized, making it easier to reproduce and verify results

ADaM Theory:

The Analysis Data Model (ADaM) provides a framework for organizing and structuring data used in statistical analysis of clinical trials. The core principles of ADaM theory include:

• Traceability: Every value in an ADaM dataset can be traced back to its origin in SDTM or raw data.

• Analysis-readiness: Datasets are structured to support statistical programming without requiring extensive pre-processing.

• Standardization: Promotes consistent data structure across studies and sponsors.

• Clarity: Variables are clearly named, defined, and documented to aid reproducibility and regulatory review.

Building ADaM datasets from SDTM involves transforming standardized clinical trial data into analysis-ready datasets that support statistical evaluation, reporting, and regulatory submission. This process is guided by CDISC standards to ensure traceability, transparency, and consistency.

Advanced ADaM programming techniques go beyond basic dataset creation, focusing on efficient, scalable, and regulatory-compliant approaches to handle complex clinical trial analyses. These techniques ensure precision, performance, and traceability in building analysis datasets for diverse and intricate statistical needs.

ADaM Metadata:

ADaM metadata refers to the detailed information that describes the structure, content, and derivations of ADaM datasets. It includes:

• Dataset-level metadata:
  Names, labels, descriptions, and class (e.g., ADSL, BDS)

• Variable-level metadata:
  Variable names, labels, data types, controlled terminology, derivation rules, origin (e.g., SDTM, derived)

• Value-level metadata (for BDS):
  Describes how specific values (like PARAM or PARAMCD) are defined, derived, or interpreted

• Derivation descriptions:
  Clear documentation of how each derived variable is calculated, supporting traceability

ADaM validation is the process of ensuring that ADaM datasets:

• Conform to CDISC ADaM standards and the ADaM Implementation Guide (IG)

• Maintain traceability to SDTM and raw data

• Support accurate and reproducible statistical analyses

Validation typically includes:

• Structure checks: Dataset and variable naming, types, formats, labels, and dataset class compliance

• Content checks: Presence of required variables (e.g., USUBJID, ASEQ, PARAMCD, AVAL)

• Derivation verification: Confirming correctness of derived variables (e.g., change from baseline, treatment flags)

• Cross-dataset consistency: Ensuring alignment across ADaM domains (e.g., ADSL and ADAE)

• Traceability checks: Ability to trace variables back to SDTM and ultimately to source data

Real-world industry case studies in ADaM highlight how pharmaceutical and biotech companies apply CDISC ADaM standards to solve complex challenges in clinical trials. These case studies demonstrate the practical application, lessons learned, and regulatory outcomes from implementing ADaM datasets across various therapeutic areas and study designs.

ADaM for Statistical Analysis:

ADaM (Analysis Data Model) datasets are specifically structured to be analysis-ready, enabling efficient, reproducible, and accurate statistical analysis of clinical trial data. These datasets are designed with:

• Pre-derived variables such as baseline values, change from baseline, flags, and time-to-event metrics

• One record per subject per analysis parameter or time point (in BDS structure), simplifying programming

• Consistent formats and naming conventions, aligned with the Statistical Analysis Plan (SAP)

ADaM enables statisticians to perform complex analyses (e.g., survival analysis, mixed models, subgroup comparisons) without needing to manipulate raw or SDTM data directly.

Industry Tools for ADaM Development:

Clinical research organizations and pharmaceutical companies rely on a range of industry-standard tools to support the development, validation, and submission of ADaM datasets

Demographics in the ADaM context refers to the subject-level information that describes the baseline characteristics of each participant in a clinical trial. This data is typically captured in the ADSL (Subject-Level Analysis Dataset) and plays a critical role in:

• Defining analysis populations (e.g., ITT, Safety, Per Protocol)

• Summarizing subject characteristics (e.g., age, sex, race, ethnicity)

• Stratifying or subgrouping data for statistical analysis

• Supporting demographic tables in clinical study reports (CSRs)

Adverse Event (AE) data in ADaM refers to the analysis-ready representation of all undesirable medical occurrences experienced by subjects during a clinical trial. This data is typically structured using the ADAE dataset, which is derived from the SDTM AE domain.

Listings are detailed, subject-level displays of clinical trial data, typically generated from ADaM datasets as part of the TLFs (Tables, Listings, and Figures) package submitted to regulatory authorities.

They serve as raw evidence for all summary results and provide complete transparency into the data collected and analyzed during the study.

Disposition in the context of TLFs (Tables, Listings, and Figures) refers to the tracking and reporting of subject participation throughout the clinical trial. It details how subjects progressed through the study, including their enrollment, treatment exposure, completion, and reasons for discontinuation.

Disposition data is typically summarized in a subject disposition table and detailed in listings, based on information captured in the ADSL (Subject-Level ADaM) dataset and sometimes supplemented by SDTM DS (Disposition) domain.

Laboratory data in TLFs (Tables, Listings, and Figures) represents clinical safety assessments based on lab test results collected during a clinical trial. These results help evaluate the impact of the investigational product on physiological and biochemical functions, and they are analyzed using standardized ADaM datasets, typically ADLB (Analysis Dataset for Laboratory Data).

Concomitant medications refer to any drugs or therapies a subject takes during a clinical trial, aside from the investigational product. These are captured in the CM (Concomitant Medications) SDTM domain and transformed into the ADCM dataset in ADaM for analysis.

They are a key component of safety evaluation and are typically reported through Listings and Summary Tables in the TLFs.

Vitals (Vital Signs) in TLFs (Tables, Listings, and Figures) refer to the routine physiological measurements collected throughout a clinical trial to assess a subject’s general health and monitor treatment-related effects.

• To evaluate the safety and tolerability of the investigational product

• To detect physiological changes or adverse reactions over time

• To assess baseline comparability between treatment arms

• To support clinical judgment and regulatory review of safety data

Figures in TLFs (Tables, Listings, and Figures) refer to graphical representations of clinical trial data, designed to visually convey trends, patterns, comparisons, and outcomes across treatment groups or time points. Figures complement tables and listings by providing intuitive insights into complex data, especially for efficacy and safety evaluations.

• To visualize key results from statistical analyses

• To reveal trends over time, treatment effects, and variability

• To highlight outliers, dose-response relationships, or survival probabilities

• To aid regulatory reviewers and medical experts in quickly interpreting trial outcomes