coxsimPoly {simPH} | R Documentation |
Simulate quantities of interest for a range of values for a polynomial nonlinear effect from Cox Proportional Hazards models
Description
coxsimPoly
simulates quantities of interest for polynomial covariate
effects estimated from Cox Proportional Hazards models. These can be plotted
with simGG
.
Usage
coxsimPoly(
obj,
b = NULL,
qi = "Relative Hazard",
pow = 2,
Xj = NULL,
Xl = NULL,
nsim = 1000,
ci = 0.95,
spin = FALSE,
extremesDrop = TRUE
)
Arguments
obj |
a |
b |
character string name of the coefficient you would like to simulate.
To find the quantity of interest using only the polynomial and not the
polynomial + the linear terms enter the polynomial created using
|
qi |
quantity of interest to simulate. Values can be
|
pow |
numeric polynomial used in |
Xj |
numeric vector of fitted values for |
Xl |
numeric vector of values to compare |
nsim |
the number of simulations to run per value of |
ci |
the proportion of simulations to keep. The default is
|
spin |
logical, whether or not to keep only the shortest probability interval rather than the middle simulations. Currently not supported for hazard rates. |
extremesDrop |
logical whether or not to drop simulated quantity of
interest values that are |
Details
Simulates quantities of interest for polynomial covariate effects.
For example if a nonlinear effect is modeled with a second order
polynomial–i.e. \beta_{1}x_{i} + \beta_{2}x_{i}^{2}
–we can draw n
simulations from the
multivariate normal distribution for both \beta_{1}
and
\beta_{2}
. Then we simply calculate quantities of interest
for a range of values and plot the results as before. For example, we find
the first difference for a second order polynomial with:
\%\triangle h_{i}(t) = (\mathrm{e}^{\beta_{1}x_{j-1} +
\beta_{2}x_{j-l}^{2}} - 1) * 100
where x_{j-l} = x_{j} - x_{l}
.
Note, you must use I
to create the polynomials.
Value
a simpoly
, coxsim
object
References
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.
Keele, Luke. 2010. ”Proportionally Difficult: Testing for Nonproportional Hazards in Cox Models.” Political Analysis 18(2): 189-205.
Carpenter, Daniel P. 2002. ”Groups, the Media, Agency Waiting Costs, and FDA Drug Approval.” American Journal of Political Science 46(3): 490-505.
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.
Liu, Ying, Andrew Gelman, and Tian Zheng. 2013. ”Simulation-Efficient Shortest Probability Intervals.” Arvix. https://arxiv.org/pdf/1302.2142v1.pdf.
See Also
simGG.simpoly
, survival
,
strata
, and coxph
Examples
# Load Carpenter (2002) data
data("CarpenterFdaData")
# Load survival package
library(survival)
# Run basic model
M1 <- coxph(Surv(acttime, censor) ~ prevgenx + lethal + deathrt1 +
acutediz + hosp01 + hhosleng + mandiz01 + femdiz01 +
peddiz01 + orphdum + natreg + I(natreg^2) +
I(natreg^3) + vandavg3 + wpnoavg3 +
condavg3 + orderent + stafcder, data = CarpenterFdaData)
# Simulate simpoly First Difference
Sim1 <- coxsimPoly(M1, b = "natreg", qi = "First Difference",
pow = 3, Xj = seq(1, 150, by = 5), nsim = 100)
## Not run:
# Simulate simpoly Hazard Ratio with spin probibility interval
Sim2 <- coxsimPoly(M1, b = "natreg", qi = "Hazard Ratio",
pow = 3, Xj = seq(1, 150, by = 5), spin = TRUE)
## End(Not run)