Why we should respect analysis results as data

TL;DR

This paper advocates for treating analysis results in clinical research as data, proposing standards and a data model to enable reuse, integration, and future analysis of these results.

Contribution

It introduces a novel data model and standards for storing analysis results, facilitating their reuse and integration across studies.

Findings

Developed a schema for analysis results data storage

Demonstrated a proof of concept for analysis results standardization

Showed potential for improved data reuse and knowledge discovery

Abstract

The development and approval of new treatments generates large volumes of results, such as summaries of efficacy and safety. However, it is commonly overlooked that analyzing clinical study data also produces data in the form of results. For example, descriptive statistics and model predictions are data. Although integrating and putting findings into context is a cornerstone of scientific work, analysis results are often neglected as a data source. Results end up stored as "data products" such as PDF documents that are not machine readable or amenable to future analysis. We propose a solution to "calculate once, use many times" by combining analysis results standards with a common data model. This analysis results data model re-frames the target of analyses from static representations of the results (e.g., tables and figures) to a data model with applications in various contexts, including knowledge discovery. Further, we provide a working proof of concept detailing how to approach analyses standardization and construct a schema to store and query analysis results.