How to Tell the Truth with Statistics: The Case for Accountable Data Analyses in Team-based Science

  • Gelfond J
  • Klugman C
  • Welty L
 et al. 
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Abstract

Data analysis is essential to translational medicine, epidemiology, and the scientific process. Although recent advances in promoting reproducibility and reporting standards have made some improvements, the data analysis process remains insufficiently documented and susceptible to avoidable errors, bias, and even fraud. Comprehensively accounting for the full analytical process requires not only records of the statistical methodology used, but also records of communications among the research team. In this regard, the data analysis process can benefit from the principle of accountability that is inherent in other disciplines such as clinical practice. We propose a novel framework for capturing the analytical narrative called the Accountable Data Analysis Process (ADAP), which allows the entire research team to participate in the analysis in a supervised and transparent way. The framework is analogous to an electronic health record in which the dataset is the "patient" and actions related to the dataset are recorded in a project management system. We discuss the design, advantages, and challenges in implementing this type of system in the context of academic health centers, where team based science increasingly demands accountability.

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Authors

  • Jonathan A L Gelfond

  • Craig M Klugman

  • Leah J Welty

  • Elizabeth Heitman

  • Christopher Louden

  • Brad H Pollock

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