predict.coxphw {coxphw}  R Documentation 
This function obtains the effect estimates (e.g. of a nonlinear or a
timedependent effect) at specified values of a continuous
covariable for a model fitted by coxphw
. It prints the
relative or log relative hazard. Additonally, the linear predictor lp
or the risk score exp(lp) can be obtained.
## S3 method for class 'coxphw' predict(object, type = c("shape", "slice.time", "slice.z", "slice.x", "lp", "risk"), x = NULL, newx = NA, refx = NA, z = NULL, at = NULL, exp = FALSE, se.fit = FALSE, pval = FALSE, digits = 4, verbose = FALSE, ...)
object 
an output object of 
type 
the type of predicted value. Choices are:

x 
name of the continuous or time variable (use "") for 
newx 
the data values for 
refx 
the reference value for variable 
z 
variable which is in interaction with 
at 
if 
exp 
if set to TRUE (default), the log relative hazard is given, otherwise the relative hazard
is requested for 
se.fit 
if set to TRUE, pointwise standard errors are produced for the predictions for

pval 
if set to TRUE add Waldtest pvalues to effect estimates at values of

digits 
number of printed digits. Default is 4. 
verbose 
if set to TRUE (default), results are printed. 
... 
further parameters. 
This function can be used to depict the estimated nonlinear or timedependent
effect of an object of class coxphw
. It supports simple nonlinear
effects as well as interaction effects of a continuous variable with a binary
covariate or with time (see examples section).
If the effect estimates of a simple nonlinear effect of x
without
interaction is requested with type = "shape"
, then x
(the usually
continuous covariate), refx
(the reference value of x
) and newx
(for these values of x
the effect estimates are obtained) must be given.
If the effect estimates of an interaction of z
with x
are requested
with type = "slice.x"
, then x
(the usually continuous variable),
z
(the categorical variable) and newx
(for these values of x
the effect estimates are obtained) must be given.
If the effect estimates of an interaction of z
with x
for one level of z
are requested with type = "slice.z"
), then x
(the usually continuous variable),
z
(the categorical variable), at
(at which level of z
),
refx
(the reference value of x
), and newx
(for these values of x
the effect estimates are obtained) must be given.
If the effect estimates of an interaction of z
with time
are requested
with type = "slice.time"
, then x
(the time
), z
(the
categorical variable) and newx
(for these values of x
the effect
estimates are obtained) must be given.
Note that if the model formula contains timebycovariate interactions, then the linear predictor and the risk score are obtained for the failure or censoring time of each subject.
If type = "shape"
, "slice.time"
, "slice.x"
, or "slice.z"
a list with the following components:
estimates 
a matrix with estimates of (log) relative hazard and corresponding confidence limits. 
se 
pointwise standard errors, only if 
p 
pvalue, only if 
alpha 
the significance level = 1  confidence level. 
exp 
an indicator if log relative hazard or relative hazard was obtained. 
x 
name of 
If type = "lp"
or "risk"
, a vector.
In coxphw version 4.0.0 the old plotshape
function is replaced with
predict.coxphw
and plot.coxphw.predict
.
Georg Heinze, Meinhard Ploner, Daniela Dunkler
Royston P and Altman D (1994). Regression Using Fractional Polynomials of Continuous Covariates: Parsimonious Parametric Modelling. Applied Statistics 43, 429467.
Royston P and Sauerbrei W (2008). Multivariable Modelbuilding. A pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables. Wiley, Chichester, UK.
### Example for type = "slice.time" data("gastric") gastric$yrs < gastric$time / 365.25 # check proportional hazards fitcox < coxph(Surv(yrs, status) ~ radiation + cluster(id), data = gastric, x = TRUE, method = "breslow") fitcox.ph < cox.zph(fit = fitcox, transform = "identity") ## compare and visualize linear and loglinear timedependent effects of radiation fit1 < coxphw(Surv(yrs, status) ~ yrs * radiation, data = gastric, template = "PH") summary(fit1) predict(fit1, type = "slice.time", x = "yrs", z = "radiation", newx = c(0.5, 1, 2), verbose = TRUE, exp = TRUE, pval = TRUE) fit2 < coxphw(Surv(yrs, status) ~ log(yrs) * radiation, data = gastric, template = "PH") summary(fit2) predict(fit2, type = "slice.time", x = "yrs", z = "radiation", newx = c(0.5, 1, 2), verbose = TRUE, exp = TRUE, pval = TRUE) plotx < seq(from = quantile(gastric$yrs, probs = 0.05), to = quantile(gastric$yrs, probs = 0.95), length = 100) y1 < predict(fit1, type = "slice.time", x = "yrs", z = "radiation", newx = plotx) y2 < predict(fit2, type = "slice.time", x = "yrs", z = "radiation", newx = plotx) plot(x = fitcox.ph, se = FALSE, xlim = c(0, 3), las = 1, lty = 3) abline(a = 0, b = 0, lty = 3) lines(x = plotx, y = y1$estimates[, "coef"], col = "red", lty = 1, lwd = 2) lines(x = plotx, y = y2$estimates[, "coef"], col = "blue", lty = 2, lwd = 2) legend(x = 1.7, y = 1.6, title = "timedependent effect", title.col = "black", legend = c("LOWESS", "linear", "loglinear"), col = c("black", "red", "blue"), lty = c(3, 1:2), bty = "n", lwd = 2, text.col = c("black", "red", "blue")) ### Example for type = "shape" set.seed(512364) n < 200 x < 1:n true.func < function(x) 2.5 * log(x)  2 x < round(runif(x) * 60 + 10, digits = 0) time < round(100000 * rexp(n= n, rate = 1) / exp(true.func(x)), digits = 1) event < rep(x = 1, times = n) my.data < data.frame(x,time,event) fit < coxphw(Surv(time, event) ~ log(x) + x, data = my.data, template = "AHR") predict(fit, type = "shape", newx = c(30, 50), refx = 40, x = "x", verbose = TRUE) plotx < seq(from = quantile(x, probs = 0.05), to = quantile(x, probs = 0.95), length = 100) plot(predict(fit, type = "shape", newx = plotx, refx = 40, x = "x")) ### Example for type = "slice.x" and "slice.z" set.seed(75315) n < 200 trt < rbinom(n = n, size = 1, prob = 0.5) x < 1:n true.func < function(x) 2.5 * log(x)  2 x < round(runif(n = x) * 60 + 10, digits = 0) time < 100 * rexp(n = n, rate = 1) / exp(true.func(x) / 4 * trt  (true.func(x) / 4)^2 * (trt==0)) event < rep(x = 1, times = n) my.data < data.frame(x, trt, time, event) fun<function(x) x^(2) fit < coxphw(Surv(time, event) ~ x * trt + fun(x) * trt , data = my.data, template = "AHR", verbose = FALSE) # plots the interaction of trt with x (the effect of trt dependent on the values of x) plotx < quantile(x, probs = 0.05):quantile(x, probs = 0.95) plot(predict(fit, type = "slice.x", x = "x", z = "trt", newx = plotx, verbose = FALSE), main = "interaction of trt with x") # plot the effect of x in subjects with trt = 0 y0 < predict(fit, type = "slice.z", x = "x", z = "trt", at = 0, newx = plotx, refx = median(x), verbose = FALSE) plot(y0, main = "effect of x in subjects with trt = 0") # plot the effect of x in subjects with trt = 1 y1 < predict(fit, type = "slice.z", x = "x", z = "trt", at = 1, newx = plotx, refx = median(x), verbose = FALSE) plot(y1, main = "effect of x in subjects with trt = 1") # Example for type = "slice.time" set.seed(23917) time < 100 * rexp(n = n, rate = 1) / exp((true.func(x) / 10)^2 / 2000 * trt + trt) event < rep(x = 1, times = n) my.data < data.frame(x, trt, time, event) plot.x < seq(from = 1, to = 100, by = 1) fun < function(t) { PT(t)^2 * log(PT(t)) } fun2 < function(t) { PT(t)^2 } fitahr < coxphw(Surv(time, event) ~ fun(time) * trt + fun2(time) * trt + x, data = my.data, template = "AHR") yahr < predict(fitahr, type = "slice.time", x = "time", z = "trt", newx = plot.x) fitph < coxphw(Surv(time, event) ~ fun(time) * trt + fun2(time) * trt + x, data = my.data, template = "PH") yph < predict(fitph, type = "slice.time", x = "time", z = "trt", newx = plot.x) plot(yahr, addci = FALSE) lines(yph$estimates$time, yph$estimates$coef, lty = 2) legend("bottomright", legend = c("AHR", "PH"), bty = "n", lty = 1:2, inset = 0.05)