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Quantifying Change Over Time: Interpreting Time-varying Effects In Duration Analyses

Published online by Cambridge University Press:  29 January 2018

Constantin Ruhe*
Affiliation:
Researcher, German Development Institute/Deutsches Institut für Entwicklungspolitik (DIE), 53113 Bonn, Germany Associated Fellow, Zukunftskolleg/Department of Political and Administrative Science, University of Konstanz, 78457 Konstanz, Germany. Email: [email protected]

Abstract

Duration analyses in political science often model nonproportional hazards through interactions with analysis time. To facilitate their interpretation, methodologists have proposed methods to visualize time-varying coefficients or hazard ratios. While these techniques are a useful, initial postestimation step, I argue that they are insufficient to identify the overall impact of a time-varying effect and may lead to faulty inference when a coefficient changes its sign. I show how even significant changes of a coefficient’s sign do not imply that the overall effect is reversed over time. In order to enable a correct interpretation of time-varying effects in this context, researchers should visualize their results with survivor functions. I outline how survivor functions are calculated for models with time-varying effects and demonstrate the need for such a nuanced interpretation using the prominent finding of a time-varying effect of mediation on interstate conflict. The reanalysis of the data using the proposed visualization methods indicates that the conclusions of earlier mediation research are misleading. The example highlights how survivor functions are an essential tool to clarify the ambiguity inherent in time-varying coefficients in event history models.

Type
Articles
Copyright
Copyright © The Author(s) 2018. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

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Footnotes

Author’s note: I would like to thank Kyle Beardsley for comments and for providing perfectly documented replication material. I would also like to thank Gerald Schneider, Adam Scharpf, Tobias Böhmelt, Nikolay Marinov, Sebastian Schutte and the participants at the European Network of Conflict Research 2015 Conference in Barcelona for their helpful input, as well as R. Michael Alvarez and the anonymous reviewers for their great feedback and critique which substantively improved this manuscript. I gratefully acknowledge funding by the German Foundation for Peace Research (Deutsche Stiftung Friedensforschung), SP06/06-2015. The replication material is available at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/4J48AX.

Contributing Editor: R. Michael Alvarez

References

Allen, S. H. 2005. The determinants of economic sanctions success and failure. International Interactions 31(2):117138.Google Scholar
Beardsley, K. 2008. Agreement without peace? International mediation and time inconsistency problems. American Journal of Political Science 52(4):723740.Google Scholar
Beardsley, K. 2011. The mediation dilemma. In Cornell studies in security affairs . Ithaca, NY: Cornell University Press.Google Scholar
Beck, N. 2010. Time is not a theoretical variable. Political Analysis 18(3):293294.Google Scholar
Beck, N., Katz, J. N., and Tucker, R.. 1998. Taking time seriously: Time-series-cross-section analysis with a binary dependent variable. American Journal of Political Science 42(4):12601288.Google Scholar
Box-Steffensmeier, J. M., and Jones, B. S.. 2004. Event history modeling: A guide for social scientists. Analytical methods for social research . New York: Cambridge University Press.Google Scholar
Box-Steffensmeier, J. M., Reiter, D., and Zorn, C. J. W.. 2003. Nonproportional hazards and event history analysis in international relations. Journal of Conflict Resolution 47(1):3353.Google Scholar
Box-Steffensmeier, J. M., and Zorn, C. J. W.. 2001. Duration models and proportional hazards in political science. American Journal of Political Science 45(4):972988.Google Scholar
Brambor, T., Clark, W. R., and Golder, M.. 2005. Understanding interaction models: Improving empirical analyses. Political Analysis 14(1):6382.Google Scholar
Carter, D. B., and Signorino, C. S.. 2010a. Back to the future: Modeling time dependence in binary data. Political Analysis 18(3):271292.Google Scholar
Carter, D. B., and Signorino, C. S.. 2010b. Reply to time is not a theoretical variable. Political Analysis 18(3):295296.Google Scholar
Chiozza, G., and Goemans, H. E.. 2004. International conflict and the tenure of leaders: Is war still ex post inefficient? American Journal of Political Science 48(3):604619.Google Scholar
Cleves, M. A., Gould, W., Gutierrez, R. G., and Marchenko, Y. V.. 2010. An introduction to survival analysis using Stata . 3 ed. College Station, TX: Stata Press.Google Scholar
Cox, D. R. 1972. Regression models and life-tables. Journal of the Royal Statistical Society Series B - Statistical Methodology 34(2):187220.Google Scholar
Crowther, M. J., and Lambert, P. C.. 2012. Simulating complex survival data. Stata Journal 12(4):674687, (14).Google Scholar
Gandrud, C. 2015. simph: An R package for illustrating estimates from cox proportional hazard models including for interactive and nonlinear effects. Journal of Statistical Software 65(3):120.Google Scholar
Golub, J. 2007. Survival analysis and European Union decision-making. European Union Politics 8(2):155179.Google Scholar
Golub, J., and Steunenberg, B.. 2007. How time affects EU decision-making. European Union Politics 8(4):555566.Google Scholar
Grewal, S., and Voeten, E.. 2015. Are new democracies better human rights compliers? International Organization 69(02):497518.Google Scholar
Hale, T. 2015. The rule of law in the global economy: Explaining intergovernmental backing for private commercial tribunals. European Journal of International Relations 21(3):483512.Google Scholar
Kalbfleisch, J. D., and Prentice, R. L.. 2002. The statistical analysis of failure time data . 2 ed. Hoboken, NJ: Wiley-Interscience.Google Scholar
Keele, L. 2010. Proportionally difficult: Testing for nonproportional hazards in Cox models. Political Analysis 18(2):189205.Google Scholar
Licht, A. A. 2011. Change comes with time: Substantive interpretation of nonproportional hazards in event history analysis. Political Analysis 19(2):227243.Google Scholar
Murillo, M. V., and Martínez-Gallardo, C.. 2007. Political competition and policy adoption: Market reforms in Latin American public utilities. American Journal of Political Science 51(1):120139.Google Scholar
Park, S., and Hendry, D. J.. 2015. Reassessing schoenfeld residual tests of proportional hazards in political science event history analyses. American Journal of Political Science 59(4):10721087.Google Scholar
Putter, H., Sasako, M., Hartgrink, H. H., van de Velde, C. J. H., and van Houwelingen, J. C.. 2005. Long-term survival with non-proportional hazards: Results from the Dutch gastric cancer trial. Statistics in Medicine 24(18):28072821.Google Scholar
Quinn, D. M., Wilkenfeld, J., Eralp, P., Asal, V., and Mclauchlin, T.. 2013. Crisis managers but not conflict resolvers: Mediating ethnic intrastate conflict in Africa. Conflict Management and Peace Science 30(4):387406.Google Scholar
Royston, P., and Parmar, M. K. B.. 2011. The use of restricted mean survival time to estimate the treatment effect in randomized clinical trials when the proportional hazards assumption is in doubt. Statistics in Medicine 30(19):24092421.Google Scholar
Ruhe, C.2017. Replication data for: Quantifying change over time: Interpreting time-varying effects in duration analyses. Harvard Dataverse, doi:10.7910/DVN/4J48AX, V1, UNF:6:NqnTUN7yBqlg7xJpgdfAmw==.Google Scholar
Ruhe, C. 2016. Estimating survival functions after stcox with time-varying coefficients. Stata Journal 16(4):867879.Google Scholar
Thomas, L., and Reyes, E. M.. 2014. Tutorial: Survival estimation for Cox regression models with time-varying coefficients using SAS and R. Journal of Statistical Software 61(1):123.Google Scholar
Werner, S., and Yuen, A.. 2005. Making and keeping peace. International Organization 59:261292.Google Scholar
Williams, L. K. 2016. Long-term effects in models with temporal dependence. Political Analysis 24(2):243262.Google Scholar
Zhelyazkova, A., and Torenvlied, R.. 2009. The time-dependent effect of conflict in the council on delays in the transposition of EU directives. European Union Politics 10(1):3562.Google Scholar
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