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Activity Number: 204
Type: Invited
Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Consulting
Abstract #310846
Title: Toward Accountable Data Analyses
Author(s): Jonathan Adam Letterman Gelfond*+ and Bradley Pollock and Elizabeth Heitman and Craig Klugman and Leah Welty and Christopher Louden
Companies: University of Texas at San Antonio and University of Texas Health Science Center at Houston and Vanderbilt University and DePaul University and Northwestern University Feinberg School of Medicine and University of Texas Health Science Center at Houston
Keywords: ethics ; accountability ; reproducibility ; reproducible research ; statistical computing
Abstract:

Data analyses are often expected to meet various types of accountability, but these remain poorly defined and misunderstood. We present a model for scientific discovery that unifies many conceptions of accountable research. Efforts to improve computational reproducibility are typically focused on the final numerical result, but they fail to consider the human interactions within the research team that are as much a part of the final result as the numerical computations. Ideally, team science should promote mutual accountability, but an objective accounting of the process of analysis requires records of context and communications. We propose a framework for organizing both the human communications and the computational results called the Accountable Data Analysis Process (ADAP). The framework is based upon a rigorous definition of accountability, which demands that communications occur according to rules within a specific type of system. We discuss the design, advantages, disadvantages, and challenges in implementing this type of system in the context of team science in such fields as clinical and translational research, but it is applicable to many research areas.


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