Research Reproducibility as a Survival Analysis

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https://arxiv.org/abs/2012.09932

Permanent Link

http://hdl.handle.net/11603/21578

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

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Author/Creator ORCID

Date

2020-12-17

Type of Work

journal articles preprints
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Citation of Original Publication

Raff, Edward; Research Reproducibility as a Survival Analysis; Machine Learning (2020); https://arxiv.org/abs/2012.09932

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Subjects

modeling the reproducibility of a paper
survival analysis problem

Abstract

There has been increasing concern within the machine learning community that we are in a reproducibility crisis. As many have begun to work on this problem, all work we are aware of treat the issue of reproducibility as an intrinsic binary property: a paper is or is not reproducible. Instead, we consider modeling the reproducibility of a paper as a survival analysis problem. We argue that this perspective represents a more accurate model of the underlying meta-science question of reproducible research, and we show how a survival analysis allows us to draw new insights that better explain prior longitudinal data. The data and code can be found at https://github.com/EdwardRaff/Research-ReproducibilitySurvival-Analysis