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simPH: An R Package for Illustrating Estimates from Cox Proportional Hazard Models Including for Interactive and Nonlinear Effects

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  • Gandrud, Christopher

Abstract

The R package simPH provides tools for effectively communicating results from Cox proportional hazard (PH) models, including models with interactive and nonlinear effects. The Cox (PH) model is a popular tool for examining event data. However, previously available computational tools have not made it easy to explore and communicate quantities of interest and associated uncertainty estimated from them. This is especially true when the effects are interactions or nonlinear transformations of continuous variables. These transformations are especially useful with Cox PH models because they can be employed to correctly specifying models that would otherwise violate the nonproportional hazards assumption. Package simPH makes it easy to simulate and then plot quantities of interest for a variety of effects estimated from Cox PH models including interactive effects, nonlinear effects, as well as standard linear effects. Package simPH employs visual weighting in order to effectively communicate estimation uncertainty. There are options to show either the standard central interval of the simulation's distribution or the shortest probability interval - which can be useful for asymmetrically distributed estimates. This paper uses hypothetical and empirical examples to illustrate package simPH 's syntax and capabilities.

Suggested Citation

  • 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, Foundation for Open Access Statistics, vol. 65(i03).
  • Handle: RePEc:jss:jstsof:v:065:i03
    DOI: http://hdl.handle.net/10.18637/jss.v065.i03
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    References listed on IDEAS

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    1. Keele, Luke, 2010. "Proportionally Difficult: Testing for Nonproportional Hazards in Cox Models," Political Analysis, Cambridge University Press, vol. 18(2), pages 189-205, April.
    2. Janet M. Box-Steffensmeier & Dan Reiter & Christopher Zorn, 2003. "Nonproportional Hazards and Event History Analysis in International Relations," Journal of Conflict Resolution, Peace Science Society (International), vol. 47(1), pages 33-53, February.
    3. Brambor, Thomas & Clark, William Roberts & Golder, Matt, 2006. "Understanding Interaction Models: Improving Empirical Analyses," Political Analysis, Cambridge University Press, vol. 14(1), pages 63-82, January.
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    3. Michael A Ruderman & Bo Kim & Kelly Stolzmann & Samantha Connolly & Christopher J Miller & Mark S Bauer, 2021. "Time course and heterogeneity of treatment effect of the collaborative chronic care model on psychiatric hospitalization rates: A survival analysis using routinely collected electronic medical records," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-17, March.
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    8. Ambrosius, Christian, 2016. "What Explains the Speed of Recovery from Banking Crises?," VfS Annual Conference 2016 (Augsburg): Demographic Change 145606, Verein für Socialpolitik / German Economic Association.
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