Often, analysis results are created and represented as static PDF documents that may contain hundreds of tables. These tables are often difficult to navigate and there can be significant variability between sponsors. Generating these reports is expensive and are typically only used once, offering limited reusability.
A Historically, typical workflow for producing analysis results may involve involves the end user generating the display in a static format such as RTF or PDF from the Analysis Data Model (ADaM) dataset. The Analysis Results Metadata (ARM) for Define-XML is XML (add reference) is then created retrospectively to provide high-level documentation about metadata relating to the analysis displays and results; however, there is no formal model or structures to describe analysis results and associated metadata, leaving a gap in standardization. The current process is expensive, time-consuming, and lacks automation and traceability, leading to unnecessary variation in analysis results reporting.
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The goal The vision for the future state of analysis results reporting is where analysis results are machine-readable, easily navigable, and highly reusable. We envision the following:
- A logical model for describing analysis and results data
- Automated generation of machine-readable results data
- Improved navigation and reusability of analysis and results data
- Support for the storage, access, processing, and reproducibility of results data
- Traceability to the study protocol/statistical analysis plan (SAP) and to input ADaM data
- Open-source tools for designing, specifying, building, and generating analysis results data
To achieve these goals, a logical model to fully describe analysis results metadata has been developed. This logical model will enable the implementation of an Analysis Results Metadata Technical Specification and an Analysis Results Data framework. ARM-TS can be used to support automation, traceability, and the creation of data displays while the ARD framework will support reuse and reproducibility of results data.
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Finally, the machine-readable analysis results dataset serves as the ‘single source of truth’ capturing the analysis results metadata and results data in a standardized format. This dataset can then be used to generate displays for multiple reporting purposes, such as traditional analysis reporting for the clinical study report (CSR), in-text tables for the CSR, safety reporting, meta-analyses, dynamic applications, ClinicalTrials.gov, publications, and presentations. This streamlined approach ensures consistency and accuracy in the generation of displays across various deliverables, making it more efficient and reliable for reporting and communication of analysis results.
- Open-source tools for designing, specifying, building, and generating analysis results data
Figure 2: Workflow with Future Extensions and Use Cases
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