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Survival Analysis
A New Guide for Social Scientists
Published online by Cambridge University Press: 03 May 2022
Summary
Quantitative social scientists use survival analysis to understand the forces that determine the duration of events. This Element provides a guideline to new techniques and models in survival analysis, particularly in three areas: non-proportional covariate effects, competing risks, and multi-state models. It also revisits models for repeated events. The Element promotes multi-state models as a unified framework for survival analysis and highlights the role of general transition probabilities as key quantities of interest that complement traditional hazard analysis. These quantities focus on the long term probabilities that units will occupy particular states conditional on their current state, and they are central in the design and implementation of policy interventions.
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- Online ISBN: 9781009053594Publisher: Cambridge University PressPrint publication: 26 May 2022
References
Beyersmann, Jan, Allignol, Arthur, and Schumacher, Martin. 2012. Competing Risks and Multistate Models with R (Use R!). New York: Springer.Google Scholar
Boehmke, Frederick J. 2009. “Policy emulation or policy convergence? Potential ambiguities in the dyadic event history approach to state policy emulation.” Journal of Politics 71(3): 1125–40.Google Scholar
Borgan, Ørnulf. 1997. Three Contributions to the Encyclopedia of Biostatistics: The Nelson-Aalen, Kaplan-Meier, and Aalen-Johansen Estimators. Preprint series. Statistical Research Report.Google Scholar
Box-Steffensmeier, Janet M. , and Jones, Bradford S.. 2004. Event History Modelling: A Guide for Social Scientists. Cambridge: Cambridge University Press.Google Scholar
Box-Steffensmeier, Janet M. , De Boef, Suzanna, and Joyce, Kyle A.. 2007. “Event dependence and heterogeneity in duration models: The conditional frailty model.” Political Analysis 15: 237–56.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
Cevasco, Kevin E. , North, Hayley M., Zeitoun, Sheryne A. et al. 2020. “COVID-19 observations and accompanying dataset of non-pharmaceutical interventions across U.S. universities, March 2020.” PLoS ONE 15(10): e0240786.Google Scholar
Cox, David R. 1972. “Regression models and life-tables.” Journal of the Royal Statistical Society B 34(2): 187–220.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): 496–509.Google Scholar
Gandrud, Christopher. 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): 1–20.Google Scholar
Goemans, Henk E. , Gleditsch, Kristian Skrede, and Chiozza, Giacomo. 2009. “Introducing Archigos: A data set of political leaders.” Journal of Peace Research 46(2): 269–83.Google Scholar
Grambsch, Patricia M. , and Therneau, Terry M.. 1994. “Proportional hazards tests and diagnostics based on weighted residuals.” Biometrika 81(3): 515–26.Google Scholar
Haller, Bernhard, Schmidt, Georg, and Ulm, Kurt. 2013. “Applying competing risks regression models: An overview.” Lifetime Data Analysis 19(1): 33–58.CrossRefGoogle ScholarPubMed
Jin, Shuai, and Boehmke, Frederick J.. 2017. “Proper specification of nonproportional hazards corrections in duration models.” Political Analysis 25: 138–44.Google Scholar
Jones, Benjamin T. , and Metzger, Shawna. 2019. “Different words, same song: Advice for substantively interpreting duration models.” PS: Political Science & Politics 52(4): 691–95.Google Scholar
Keele, Luke. 2010. “Proportionally difficult: Testing for nonproportional hazards in Cox models.” Political Analysis 18(2): 189–205.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
Maeda, Ko. 2010. “Two modes of democratic breakdown: A competing risks analysis of democratic durability.” Journal of Politics 72(4): 1129–43.CrossRefGoogle Scholar
Metzger, Shawna K. , and Jones, Benjamin T.. 2016. “Surviving phases: Introducing multistate survival models.” Political Analysis 24(4): 457–77.Google Scholar
Metzger, Shawna K. , and Jones, Benjamin T.. 2021. “Properly calculating estat phtest in the presence of stratified hazards.” The Stata Journal 21(4): 1028–33.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
Przeworski, Adam, Alvarez, Michael E., Cheibub, Jose Antonio, and Limongi, Fernando. 2000. Democracy and Development: Political Institutions and Well-Being in the World, 1950–1990. Cambridge: Cambridge University Press.Google Scholar
Putter, Hein. 2021. Tutorial in Biostatistics: Competing Risks and Multi-state Models. Analyses Using the mstata Package. Comprehensive R Archive Network (CRAN). https://cran.r-project.org/web/packages/mstate/vignettes/Tutorial.pdfGoogle Scholar
Putter, Hein, Fiocco, Marta, and Geskus, Ronald B.. 2007. “Tutorial in biostatistics: Competing risks and multi-state models.” Statistics in Medicine 26(11): 2389–430.CrossRefGoogle ScholarPubMed
Quiroz Flores, Alejandro, Liza, Farhana, Quteineh, Husam, and Czarnecka, Barbara 2021. “Variation in the timing of Covid-19 communication across universities in the UK.” PLoS ONE 16(2): e0246391.Google Scholar
Michael, Schemper. 1992. “Cox analysis of survival data with non-proportional hazard functions.” Journal of the Royal Statistical Society: Series D 41(4): 455–65.Google Scholar
Therneau, Terry M. 2020. Package “coxme”: Mixed Effects Cox Models. Comprehensive R Archive Network (CRAN). https://cran.r-project.org/web/packages/coxme/index.htmlGoogle Scholar
Therneau, Terry M. , Crowson, Cynthia, and Atkinson, Elizabeth. 2021. Multi-state Models and Competing Risks. Comprehensive R Archive Network (CRAN). https://cran.r-project.org/web/packages/survival/vignettes/compete.pdfGoogle Scholar
Therneau, Terry M. , and Grambsch, Patricia M.. 2000. Modeling Survival Data: Extending the Cox Model. New York: Springer.Google Scholar
Therneau, Terry M. , Lumley, Thomas, Elizabeth, Atkinson, and Cynthia, Crowson. 2022. Package “survival”: Survival Analysis. Comprehensive R Archive Network (CRAN). https://cran.r-project.org/web/packages/survival/index.htmlGoogle Scholar
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