  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-Reproducibility-Survival-Analysis
