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Surviving Phases: Introducing Multistate Survival Models

Published online by Cambridge University Press:  04 January 2017

Shawna K. Metzger*
Affiliation:
University Scholars Programme 18 College Avenue East #02-03 Cinnamon West Learn Lobe Singapore 138593 Singapore
Benjamin T. Jones
Affiliation:
Department of Political Science P.O. Box 1848 University of Mississippi University, MS 38677, USA e-mail: [email protected]

Abstract

Many political processes consist of a series of theoretically meaningful transitions across discrete phases that occur through time. Yet political scientists are often theoretically interested in studying not just individual transitions between phases, but also the duration that subjects spend within phases, as well as the effect of covariates on subjects’ trajectories through the process's multiple phases. We introduce the multistate survival model to political scientists, which is capable of modeling precisely this type of situation. The model is appealing because of its ability to accommodate multiple forms of causal complexity that unfold over time. In particular, we highlight three attractive features of multistate models: transition-specific baseline hazards, transition-specific covariate effects, and the ability to estimate transition probabilities. We provide two applications to illustrate these features.

Type
Articles
Copyright
Copyright © The Author 2016. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors’ note: The authors’ names appear in reverse alphabetical order. This paper was presented at the 2015 Visions in Methodology conference. We thank Jan Box-Steffensmeier, Sarah Cormack-Patton, Luke Keele, Diana O’Brien, Steve Oliver, and Doug Rice for feedback on earlier drafts. We bear sole responsibility for any remaining errors and shortcomings. All analyses are performed using R 3.3.1. Replication material is available at http://dx.doi.org/10.7910/DVN/OZ7YZ1. Supplementary materials for this article are available on the Political Analysis Web site.

References

Aalen, Odd, Borgan, Ornulf, and Gjessing, Hakon. 2008. Survival and event history analysis: A process point of view. New York: Springer.Google Scholar
Allison, Paul D. 1984. Event history analysis: Regression for longitudinal event data, 1st ed. Thousand Oaks, CA: Sage.Google Scholar
Beck, Nathaniel, Katz, Jonathan, and Tucker, Richard. 1998. Taking time seriously: Time-series-cross-section analysis with a binary dependent variable. American Journal of Political Science 42(4):1260–88.Google Scholar
Beyersmann, Jan, Allignol, Arthur, and Schumacher, Martin. 2011. Competing risks and multistate models with R. New York: Springer.Google Scholar
Box-Steffensmeier, Janet M., and Jones, Bradford S. 2004. Event history modeling: A guide for social scientists. Cambridge, UK Cambridge University Press.CrossRefGoogle Scholar
Box-Steffensmeier, Janet M., Linn, Suzanna, and Smidt, Corwin D. 2014. Analyzing the robustness of semi-parametric duration models for the study of repeated events. Political Analysis 22(2):183204.Google Scholar
Box-Steffensmeier, Janet M., and Zorn, Christopher J. W. 2002. Duration models for repeated events. Journal of Politics 64(4):1069–94.Google Scholar
Brambor, Thomas, Roberts Clark, William, and Golder, Matt. 2006. Understanding interaction models: Improving empirical analyses. Political Analysis 14(1):6382.Google Scholar
Braumoeller, Bear F. 2003. Causal complexity and the study of politics. Political Analysis 11(3):209–33.Google Scholar
Carter, David B., and Signorino, Curtis S. 2010. Back to the future: Modeling time dependence in binary data. Political Analysis 18(3):271–92.Google Scholar
Chiba, Daina, Metternich, Nils W., and Ward, Michael D. 2015. Every story has a beginning, middle, and an end (but not always in that order): Predicting duration dynamics in a unified framework. Political Science Research and Methods 3(3):515–41.Google Scholar
Cleves, Mario, Gould, William, Gutierrez, Roberto, and Marchenko, Yulia. 2010. An introduction to survival analysis using Stata, 3rd ed. College Station, TX: Stata Press.Google Scholar
Cox, David R. 1972. Regression models and life-tables. Journal of the Royal Statistical Society B 34(2):187220.Google Scholar
Crowder, Martin J. 2012. Multivariate survival analysis and competing risks. Boca Raton, FL: Chapman and Hall/CRC.Google Scholar
Daniel, William T. 2015. Career behaviour and the European parliament: All roads lead through Brussels? Oxford: Oxford University Press.Google Scholar
Epstein, David L., Bates, Robert, Goldstone, Jack, Kristensen, Ida, and O’Halloran, Sharyn. 2006. Democratic transitions. American Journal of Political Science 50(3):551–69.Google Scholar
Fine, Jason P., and Gray, Robert J. 1999. A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association 94(446):496509.Google Scholar
Fukumoto, Kentaro. 2009. Systematically dependent competing risks and strategic retirement. American Journal of Political Science 53(3):740–54.CrossRefGoogle Scholar
Geskus, Ronald B. 2015. Data analysis with competing risks and intermediate states. Boca Raton, FL: Chapman and Hall/CRC.Google Scholar
Gordon, Sanford C. 2002. Stochastic dependence in competing risks. American Journal of Political Science 46(1):200–17.CrossRefGoogle Scholar
Greene, William H. 2012. Econometric analysis, 7th ed. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Hansen, Holley E., McLaughlin Mitchell, Sara, and Nemeth, Stephen C. 2008. IO mediation of interstate conflicts: Moving beyond the global versus regional dichotomy. Journal of Conflict Resolution 52(2):295325.Google Scholar
Hansford, Thomas G., Carol Savchak, Elisha, and Songer, Donald R. 2010. Politics, careerism, and the voluntary departures of U.S. District Court judges. American Politics Research 38(6):9861014.Google Scholar
Hensel, Paul R. 2001. Contentious issues and world politics: The management of territorial claims in the Americas, 1816–1992. International Studies Quarterly 45(1):81109.Google Scholar
Hougaard, Philip. 2000. Analysis of multivariate survival data. New York: Springer.Google Scholar
Huth, Paul K., and Allee, Todd L. 2002. The democratic peace and territorial conflict in the twentieth century. Cambridge, UK Cambridge University Press.Google Scholar
Jackson, Christopher H. 2011. Multi-state models for panel data: The msm package for R. Journal of Statistical Software 38(8):128.Google Scholar
Jones, Benjamin T. 2013. The past is ever-present: Civil war as a dynamic process. PhD dissertation, The Ohio State University.Google Scholar
Jones, Benjamin T., and Metzger, Shawna K. Forthcoming. Evaluating conflict dynamics: A novel empirical approach to stage conceptions. Journal of Conflict Resolution.Google Scholar
Keele, Luke. 2010. Proportionally difficult: Testing for nonproportional hazards in Cox models. Political Analysis 18(2):189205.Google Scholar
Licht, Amanda A. 2011. Change comes with time: Substantive interpretation of nonproportional hazards in event history analysis. Political Analysis 19(2):227–43.Google Scholar
Linz, Juan J. and Stepan, Alfred, eds. 1978. The breakdown of democratic regimes. Baltimore, MD: Johns Hopkins University Press.Google Scholar
Linz, Juan J. and Stepan, Alfred, eds. 1996. Problems of democratic transition and consolidation: Southern Europe, South America, and post-communist Europe. Baltimore, MD; Johns Hopkins University Press.Google Scholar
Maeda, Ko. 2010. Two modes of democratic breakdown: A competing risks analysis of democratic durability. Journal of Politics 72(4):1129–43.Google Scholar
Mattiacci, Eleonora, and Jones, Benjamin T. 2016. (Nuclear) change of plans: What explains nuclear reversals? International Interactions 42(3):530–58.Google Scholar
Metzger, Shawna K., and Jones, Benjamin T. 2016. Replication data for ‘surviving phases: Introducing multistate survival models.’ Harvard Dataverse.Google Scholar
Mills, Melinda. 2004. Stability and change: The structuration of partnership histories in Canada, the Netherlands, and the Russian Federation. European Journal of Population 20(2):141–75.Google Scholar
O’Donnell, Guillermo, and Schmitter, Philippe C. 1986. Transitions from authoritarian rule: Tentative conclusions about uncertain democracies. Baltimore, MD: Johns Hopkins University Press.Google Scholar
Park, Sunhee, and Hendry, David J. 2015. Reassessing Schoenfeld residual tests of proportional hazards in political science event history analyses. American Journal of Political Science 59(4):1072–87.Google Scholar
Prentice, R. L., Williams, B. J., Peterson, and A. V. 1981. On the regression analysis of multivariate failure time data. Biometrika 68(2):373–79.Google Scholar
Putter, H., Fiocco, M., Geskus, and R. B. 2007. Tutorial in biostatistics: Competing risks and multi-state models. Statistics in Medicine 26(11):2389–430.Google Scholar
Reed, William. 2000. A unified statistical model of conflict onset and escalation. American Journal of Political Science 44(1):8493.Google Scholar
Slantchev, Branislav L. 2004. How initiators end their wars: The duration of warfare and the terms of peace. American Journal of Political Science 48(4):813–29.Google Scholar
Svolik, Milan W. 2015. Which democracies will last? Coups, incumbent takeovers, and the dynamic of democratic consolidation. British Journal of Political Science 45(4):715–38.Google Scholar
Therneau, Terry M., and Grambsch, Patricia M. 2000. Modeling survival data: Extending the Cox model. New York: Springer.Google Scholar
Vollset, Stein Emil, Tverdal, Aage, and Gjessing, Håkon K. 2006. Smoking and deaths between 40 and 70 years of age in women and men. Annals of Internal Medicine 144(6):381–89.Google ScholarPubMed
Wreede, Liesbeth C. de, Fiocco, Marta, and Putter, Hein. 2010. The mstate package for estimation and prediction in non- and semi-parametric multi-state and competing risks models. Computer Methods and Programs in Biomedicine 99(3):261–74.Google Scholar
Wreede, Liesbeth C. de, Fiocco, Marta, and Putter, Hein. 2011. mstate: An R package for the analysis of competing risks and multi-state models. Journal of Statistical Software 38(7):130.Google Scholar
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