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Analyzing the Robustness of Semi-Parametric Duration Models for the Study of Repeated Events

Published online by Cambridge University Press:  04 January 2017

Janet M. Box-Steffensmeier ,
Suzanna Linn  and
Corwin D. Smidt
Janet M. Box-Steffensmeier
Affiliation:
Department of Political Science, Ohio State University, 2140 Derby Hall, 154 N. Oval Mall Columbus, OH 43210
Suzanna Linn*
Affiliation:
Department of Political Science, Penn State University, 320 Pond Lab, University Park, PA 16802
Corwin D. Smidt
Affiliation:
Department of Political Science, Michigan State University, South Kedzie Hall, 368 Farm Lane, S303, East Lansing, MI 48824
*
e-mail: slinn@la.psu.edu (corresponding author)
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Abstract

Estimators within the Cox family are often used to estimate models for repeated events. Yet, there is much we still do not know about the performance of these estimators. In particular, we do not know how they perform given time dependence, different censoring rates, and a varying number of events and sample sizes. We use Monte Carlo simulations to demonstrate the performance of a variety of popular semi-parametric estimators as these data aspects change and under conditions of event dependence and heterogeneity, both, or neither. We conclude that the conditional frailty model outperforms other standard estimators under a wide array of data-generating processes, and data limitations rarely alter its performance.

Type
Research Article
Information
Political Analysis , Volume 22 , Issue 2 , Spring 2014 , pp. 183 - 204
Copyright
Copyright © The Author 2013. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' note: Thanks to Neal Beck and anonymous reviewers for helpful comments on drafts of the article.

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