论文标题
为什么我们应该尊重分析结果作为数据
Why we should respect analysis results as data
论文作者
论文摘要
新疗法的开发和批准会产生大量结果，例如功效和安全性摘要。但是，通常忽略了分析临床研究数据还以结果形式产生数据。例如，描述性统计和模型预测是数据。尽管将发现并将发现纳入上下文是科学工作的基石，但分析结果通常被忽略为数据源。结果最终被存储为“数据产品”，例如不可读取或不适合将来分析的PDF文档。我们建议通过将分析结果标准与通用数据模型相结合，以“计算一次，多次使用”。该分析结果数据模型从结果（例如表和图形）的静态表示（例如表和图）重新构建了分析的目标，以在各种情况下的应用程序（包括知识发现）中进行应用。此外，我们提供了概念的工作证明，详细介绍了如何使用分析标准化并构建图架以存储和查询分析结果。
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.
