Description Usage Arguments Details Value Examples
View source: R/predict.PHcure.R
Compute probabilities to be susceptible and survival probabilities (conditional on being susceptible) for a model fitted by penPHcure
with the argument pen.type = "none"
.
1 2 
object 
an object of class 
newdata 
a data.frame in counting process format. 
X 
[optional] a matrix of timeinvariant covariates. It is not required, unless argument 
... 
ellipsis to pass extra arguments. 
If argument X
was not supplied in the call to the penPHcure
function, the probabilities to be susceptible are computed using the covariates retrieved using the same which.X
method as in the penPHcure
function call.
An object of class predict.PHcure
, a list including the following elements:

a numeric vector containing the probabilities to be susceptible to the event of interest: P(Y_i=1x_i) = \frac{ e^{\mathbf{x}_i'\hat{\mathbf{b}}} }{1+e^{\mathbf{x}_i'\hat{\mathbf{b}}}}, where \mathbf{x}_i is a vector of timeinvariant covariates and \hat{\mathbf{b}} is a vector of estimated coefficients. 

a numeric vector containing the survival probabilities (conditional on being susceptible to the event of interest): S(t_iY_i=1,\bar{\mathbf{z}}_i(t))=\exp≤ft(∑_{j=1}^K (t_{(j1)}t_{(j)}) \hat{λ}_{0j} I(t_{(j)}≤q t_i) e^{\mathbf{z}_i(t_{(j)})\hat{\boldsymbol{β}}}\right), where t_{(1)}<t_{(2)}<...<t_{(K)} denotes the K ordered eventtimes, \mathbf{z}_i(t) is a vector of timevarying covariates, \hat{\boldsymbol{β}} is a vector of estimated coefficients and \hat{λ}_{0j} is the estimated baseline hazard function (constant in the interval (t_{(j1)},t_{(j)}]). 
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25  # Generate some data (for more details type ?penPHcure.simulate in your console)
set.seed(12) # For reproducibility
data < penPHcure.simulate(N=250)
# Fit standard cure model (without inference)
fit < penPHcure(Surv(time = tstart,time2 = tstop,
event = status) ~ z.1 + z.2 + z.3 + z.4,
cureform = ~ x.1 + x.2 + x.3 + x.4,data = data)
# Use the predict method to obtain the probabilities for the fitted model
pred.fit < predict(fit,data)
# Use the predict method to make prediction for new observations.
# For example, two individuals censored at time 0.5 and 1.2, respectively,
# and all cavariates equal to 1.
newdata < data.frame(tstart=c(0,0),tstop=c(0.5,1.2),status=c(0,0),
z.1=c(1,1),z.2=c(1,1),z.3=c(1,1),z.4=c(1,1),
x.1=c(1,1),x.2=c(1,1),x.3=c(1,1),x.4=c(1,1))
pred.fit.newdata < predict(fit,newdata)
# The probabilities to be susceptible are:
pred.fit.newdata$CURE
# [1] 0.6761677 0.6761677
# The survival probabilities (conditional on being susceptible) are:
pred.fit.newdata$SURV
# [1] 0.5591570 0.1379086

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