predict.tvcure: Predict method for tvcure model fits

In tvcure: Additive Cure Survival Model with Time-Varying Covariates

Predict method for tvcure model fits

Description

Predicted values based on a tvcure object.

Usage

## S3 method for class 'tvcure'
predict(object, newdata, ci.level=.95, ...)

Arguments

object	A tvcure.object.
newdata	A data frame in which to look for the 'id' (distinguishing the different units), 'time' and covariate values for which 'predictions' should be made. Time values for a given 'id' should be a series of consecutive integers starting with 1. If newdata$id does not exist, then predictions are assumed to concern a single unit with consecutive time values starting with 1.
ci.level	Credible level for the reported estimates. (Default: 0.95).
...	additional generic arguments.

Value

A data frame containing, in addition to the optional newdata entries, the following elements:

• Hp : Matrix containing estimates of the cumulative population hazard H_p(t|x_{1:t}) with its credible interval bounds at time t given the history of covariates.

• lHp : Matrix containing estimates of the log cumulative population hazard \log H_p(t|x_{1:t}) with its standard error and credible interval bounds at time t given the history of covariates.

• se.lHp : Vector containing the standard errors of the estimated log cumulative population hazard at time t given the history of covariates.

• hp : Matrix containing estimates of the population hazard h_p(t|x_{1:t}) with its credible interval bounds at time t given the history of covariates.

• lhp : Matrix containing estimates of the log population hazard \log h_p(t|x_{1:t}) with its standard error and credible interval bounds at time t given the history of covariates.

• se.lhp : Vector containing the standard errors of the estimated log population hazard at time t given the history of covariates.

• Sp : Matrix containing estimates of the population survival fuction S_p(t|x_{1:t})=\exp(-H_p(t|x_{1:t})) with its credible interval bounds at time t given the history of covariates.

• pcure : Matrix containing estimates of the conditional cure probability of a unit still at tisk at time t, P(T=+\infty|T>t,x=x_t), with its credible interval bounds at time t if covariates remain constant from time t.

• llpcure : Matrix containing estimates of the conditional log-log cure probability of a unit still at tisk at time t, \log(-\log P(T=+\infty|T>t,x=x_t)), with its standard error and credible interval bounds at time t if covariates remain constant from time t.

• se.llpcure : Vector containing the standard errors of the estimated conditional log-log cure probability of a unit still at tisk at time t, \log(-\log P(T=+\infty|T>t,x=x_t)), if covariates remain constant from time t.

Author(s)

Philippe Lambert [e-mail]

References

Lambert, P. and Kreyenfeld, M. (2025). Time-varying exogenous covariates with frequently changing values in double additive cure survival model: an application to fertility. Journal of the Royal Statistical Society, Series A. <doi:10.1093/jrsssa/qnaf035>

Examples

require(tvcure)
## Simulated data generation
beta = c(beta0=.4, beta1=-.2, beta2=.15) ; gam = c(gam1=.2, gam2=.2)
data = simulateTVcureData(n=500, seed=123, beta=beta, gam=gam,
                          RC.dist="exponential",mu.cens=550)$rawdata
## TVcure model fitting
tau.0 = 2.7 ; lambda1.0 = c(40,15) ; lambda2.0 = c(25,70) ## Optional
model = tvcure(~z1+z2+s(x1)+s(x2), ~z3+z4+s(x3)+s(x4), data=data,
               tau.0=tau.0, lambda1.0=lambda1.0, lambda2.0=lambda2.0)

## Covariate profiles for which 'predicted' values are requested
newdata = subset(data, id==1 | id==4)[,-3] ## Focus on units 1 & 4
pred = predict(model,newdata)

## Visualize the estimated population survival fns for units 1 & 4
## par(mfrow=c(1,2))
with(subset(pred,id==1), plotRegion(time,Sp,main="Id=1",
                              ylim=c(0,1),xlab="t",ylab="Sp(t)"))
with(subset(pred,id==4), plotRegion(time,Sp,main="Id=4",
                              ylim=c(0,1),xlab="t",ylab="Sp(t)"))

tvcure documentation built on April 12, 2025, 1:58 a.m.