A Statistical Evaluation of Repeated Events Models with an Application to the Study of the Democratic Peace

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Scholars have long known that multiple events data, which occur when subjects experience more than one event, cause a problem when analyzed without taking into consideration the correlation among the events. In particular, there has not been a solution about the best way to model the common occurrence of repeated events, where the subject experiences the same type of event more than once. This research project will result in an assessment of whether one of the two main approaches for the study of repeated events, variance corrected or frailty, is better able to account for within-subject correlation. Monte Carlo evidence will help determine whether and under what conditions alternative modeling strategies for repeated events are appropriate. Next, the project will compare frailty and multi-level frailty models by examining the results of a standard hazard model with no correction for clustering, three single frailty effect models to allow for clustering, and finally a model based on a cross-classified frailty model that allows for clustering by all three levels. Finally, the project will investigate the treatment of missing data in analyses of heterogeneity. Simulations will be rerun comparing variance corrected and frailty models with the complication of missing data. The statistical work resulting from this project will help resolve debates about political dynamics, such as the liberal peace, by commenting on the reliability of the different modeling strategies used to test those theories and applying the models discussed. The fellowship period will allow me to deepen my understanding of event history models and acquire new skills in the areas of Monte Carlo simulations, Bayesian analysis, and computer programming.The question as to the best modeling strategy for repeated events data is an important one. Our understanding of political processes, as in all studies, depends on the quality of the inferences we can draw from our models. There is currently little guidance about which approach or model is appropriate and so, not surprisingly, we see analysts unsure of the best way to analyze their data. Given the dramatic substantive differences that result from using the different models and approaches, this is a problem that will be of interest across research communities. This research is supported by the Methodology, Measurement, and Statistics Program and the Statistics and Probability Program under the Mid-Career Methodological Opportunities Fellowship Announcement.