Christopher Gandrud
https://github.com/christophergandrud/simPH/issues
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 in the Journal of Statistical Software.
To cite the paper please use:
@article{simPH_JSS,
author = {Christopher Gandrud},
title = {simPH: An R Package for Illustrating Estimates from Cox
Proportional Hazard Models Including for Interactive and Nonlinear
Effects},
journal = {Journal of Statistical Software},
year = {2015},
volume = {65},
issue = {3},
pages = {1--20}
}
The package includes the following functions:
coxsimLinear
: a function for simulating relative hazards, first differences,
hazard ratios, and hazard rates for linear, non-time interacted covariates from
Cox Proportional Hazard models.
coxsimtvc
: a function for simulating time interactive hazards (relative
hazards, first differences, and hazard ratios) for covariates from Cox
Proportional Hazard models. The function will calculate time-interactive hazard
ratios for multiple strata estimated from a stratified Cox Proportional Hazard
model.
coxsimSpline
: a function for simulating quantities of interest from
penalised splines using multivariate normal distributions. Currently does not
support simulating hazard rates from stratified models. Note: be extremely
careful about the number of simulations you ask the function to find. It is very
easy to ask for more than your computer can handle.
coxsimPoly
: a function for simulating quantities of interest for a range of
values for a polynomial nonlinear effect from Cox Proportional Hazard models.
coxsimInteract
: a function for simulating quantities of interest for linear
multiplicative interactions, including marginal effects and hazard rates.
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:
simGG.simlinear
: plots simulated linear time-constant hazards using
ggplot2.
simGG.simtvc
: uses ggplot2 to graph the simulated time-varying relative
hazards, first differences, hazard ratios or stratified hazard rates.
simGG.simspline
: uses ggplot2 to plot
quantities of interest from simspline
objects, including relative hazards,
first differences, hazard ratios, and hazard rates.
simGG.simpoly
: uses ggplot2 to graph the simulated polynomial quantities
of interest.
simGG.siminteract
: uses ggplot2 to graph linear multiplicative
interactions.
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.
SurvExpand
: a function for converting a data frame of non-equal interval
continuous observations into equal interval continuous observations. This is
useful to do before creating time interactions.
tvc
: a function for creating time interactions. Currently supports
'linear'
, natural 'log'
, and exponentiation ('power'
).
setXl
: a function for setting valid Xl
values given a sequence of fitted
Xj
values. This makes it more intuitive to find hazard ratios and first
differences for comparisons between some Xj fitted values and Xl values other
than 0.
ggfitStrata
: a function to plot fitted stratified survival curves estimated
from survfit
using ggplot2. This function builds on the survival
package's plot.survfit
function. One major advantage is the ability to split
the survival curves into multiple plots and arrange them in a grid. This makes
it easier to examine many strata at once. Otherwise they can be very bunched up.
MinMaxLines
: a function for summarising the constricted intervals from the
simulations, including the median, upper and lower bounds and
the middle 50% of these intervals.
The package is available on CRAN and can be installed in the normal R way.
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.
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.
For more information about stratified Cox PH models 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.
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. (2015). "Simulation-efficient Shortest Probablility Intervals." Statistics and Computing 25:809-819.
Also good: Hyndman, R. J. (1996). "Computing and Graphing Highest Density Regions." The American Statistician, 50(2): 120–126.
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.
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 developed this package):
Gandrud, Christopher. 2013. “The Diffusion of Financial Supervisory Governance Ideas.” Review of International Political Economy. 20(4): 881-916.
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.
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