Large trials and studies generate many analysis results in the form of tables, figures and written reports. Historically, a typical workflow for producing analysis results involves the end user generating the display in a static format such as RTF or PDF from the Analysis Data Model (ADaM) dataset (Figure 1). The Analysis Results Metadata (ARM) for Define-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, lacks automation and traceability, leading to unnecessary variation in analysis results reporting.
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Figure 1: Example of Current
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Workflow
The goal for the future state of analysis results is that they are machine-readable, easily navigable, and highly reusable. Our aim was to create a logical model that fully described analysis results and associated metadata to support the following objectives:
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The Analysis Results Standard (ARS) Model has several possible implementations including leveraging analysis results metadata to aid in automation as well as representing analysis results as data in a dataset structure. The creation of an ARS technical specification could be used support automation, traceability, and the creation of data displays. An analysis results dataset could support reuse and reproducibility of results data. The following is an example of how the ARS Model could be used in a modernized workflow that shifts the focus from retrospective reporting to prospective planning (Figure 2).
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Figure 2: Example of Potential Future Workflow
First, an end-users user could use the ARS Model to guide in the generation of a technical specification prior to generating a display, rather than after the display has been created. This approach will allow for better planning and standardization of the analysis process, resulting in more consistent and traceable reporting. The technical specification could include metadata about the statistical methods, data sources, and displays to be generated. Once the technical specification has been developed, the end-user can use it to generate an analysis results dataset, which contains the results data needed to generate the display. The analysis results dataset could be designed to support reuse and reproducibility of the results data, enabling more efficient and effective analysis reporting.
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Overall, this new workflow would enable end-users to generate analysis results metadata prospectively, with greater standardization, consistency, and traceability of analysis results reporting, enabling better decision-making and regulatory submissions. Shifting the focus from retrospective reporting to prospective planning would help to address many of the current limitations of analysis results reporting and support the development of more efficient and effective analysis standards.
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Use of LinkML for the development of the ARS Logical Model
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