Christopher Gandrud

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simPH is an R package for simulating and plotting quantities of interest (relative hazards, first differences, and hazard ratios) for linear coefficients, multiplicative interactions, polynomials, penalised splines, and non-proportional hazards, as well as stratified survival curves from Cox Proportional Hazard models.

For more information plus examples, please see the description paper forthcoming in the Journal of Statistical Software.

To cite the paper please use:

    author = {Christopher Gandrud},
    title = {simPH: An R Package for Illustrating Estimates from Cox
        Proportional Hazard Models Including for Interactive and Nonlinear
    journal = {Journal of Statistical Software},
    year = {2015},
    volume = {65},
    issue = {3},
    pages = {1--20}


The package includes the following functions:

Simulation Functions

Plotting Functions

Results from these functions can be plotted using the simGG method. The syntax and capabilities of simGG varies depending on the sim object class you are using:

Additional styling

Because in almost all cases simGG returns a ggplot2 object, you can add additional aesthetic attributes in the normal ggplot2 way. See the ggplot2 documentation for more details.



The package is available on CRAN and can be installed in the normal R way.

To install the development version use the devtools function install_github. Here is the code for installing the most recent development version:



Before running the simulation and graph functions in this package carefully consider how many simulations you are about to make. Especially for hazard rates over long periods of time and with multiple strata, you can be asking simPH to run very many simulations. This will be computationally intensive.


Simulating Parameter Estimates

For more information about simulating parameter estimates to make interpretation of results easier see:

Licht, Amanda A. 2011. “Change Comes with Time: Substantive Interpretation of Nonproportional Hazards in Event History Analysis.” Political Analysis 19: 227–43.

King, Gary, Michael Tomz, and Jason Wittenberg. 2000. “Making the Most of Statistical Analyses: Improving Interpretation and Presentation.” American Journal of Political Science 44(2): 347–61.

Stratified Cox PH

For more information about stratified Cox PH models (and frailties, which I am working to incorporate in future versions) see:

Box-Steffensmeier, Janet M, and Suzanna De Boef. 2006. “Repeated Events Survival Models: the Conditional Frailty Model.” Statistics in Medicine 25(20): 3518–33.

Shortest Probability Intervals

To learn more about shortest probability intervals (and also for the source of the code that made this possible in simPH) see:

Liu, Y., Gelman, A., & Zheng, T. (2013). "Simulation-efficient Shortest Probablility Intervals." Arvix.

Also good: Hyndman, R. J. (1996). "Computing and Graphing Highest Density Regions." The American Statistician, 50(2): 120–126.

Interpreting Interactions

For more information about interpreting interaction terms:

Brambor, Thomas, William Roberts Clark, and Matt Golder. 2006. “Understanding Interaction Models: Improving Empirical Analyses.” Political Analysis 14(1): 63–82.

The Olden Days

For an example of how non-proportional hazard results were often presented before simPH see (some of the problems I encountered in this paper were a major part of why I'm developing this package):

Gandrud, Christopher. 2013. “The Diffusion of Financial Supervisory Governance Ideas.” Review of International Political Economy. 20(4): 881-916.

Future Plans

I intend to expand the quantities of interest that can be simulated and graphed for Cox PH models. I am also currently working on functions that can simulate and graph hazard ratios estimated from Fine and Gray competing risks models.

I am also working on a way to graph hazard ratios with frailties.

Licensed under GPL-3