Published online by Cambridge University Press: 25 March 2019
We develop a new Bayesian split population survival model for the analysis of survival data with misclassified event failures. Within political science survival data, right-censored survival cases are often erroneously misclassified as failure cases due to measurement error. Treating these cases as failure events within survival analyses will underestimate the duration of some events. This will bias coefficient estimates, especially in situations where such misclassification is associated with covariates of interest. Our split population survival estimator addresses this challenge by using a system of two equations to explicitly model the misclassification of failure events alongside a parametric survival process of interest. After deriving this model, we use Bayesian estimation via slice sampling to evaluate its performance with simulated data, and in several political science applications. We find that our proposed “misclassified failure” survival model allows researchers to accurately account for misclassified failure events within the contexts of civil war duration and democratic survival.
Authors’ note: Earlier versions of this paper were presented as a poster at the 2014 Society For Political Methodology Annual Meeting in Athens, GA, USA and the 2018 Asian Political Methodology Conference in Seoul, South Korea, and as a paper at the 2018 Penn State Department of Political Science Brown Bag seminar series in State College, PA, USA, and the 2018 Visions in Methodology Conference at Ohio State University, in Columbus, OH, USA. Bagozzi’s contribution is partly based upon the work supported by the National Science Foundation under Grant Nos. SBE-SMA-1539302 and DMS-1737865. The authors wish to thank Jeff Gill, two anonymous reviewers, Lee Ann Banaszak, Liz Carlson, Bruce Desmarais, Kentaro Fukumoto, Simon Heuberger, Nahomi Ichino, Kosuke Imai, Azusa Katagiri, Fridolin Linder, Elizabeth Menninga, Shawna K. Metzger, Sara Mitchell, Inken von Borzyskowski, Michael Ward, Joe Wright, and Teppei Yamamoto for their helpful comments and suggestions. See Bagozzi et al. (2019) for replication materials.
Contributing Editor: Jeff Gill