predict.survPen: Hazard and Survival prediction from fitted 'survPen' model

In survPen: Multidimensional Penalized Splines for Survival and Net Survival Models

Hazard and Survival prediction from fitted survPen model

Description

Takes a fitted survPen object and produces hazard and survival predictions given a new set of values for the model covariates.

Usage

## S3 method for class 'survPen'
predict(
  object,
  newdata,
  newdata.ref = NULL,
  n.legendre = 50,
  conf.int = 0.95,
  do.surv = TRUE,
  type = "standard",
  exclude.random = FALSE,
  get.deriv.H = FALSE,
  ...
)

Arguments

object	a fitted survPen object as produced by survPen.fit
newdata	data frame giving the new covariates value
newdata.ref	data frame giving the new covariates value for the reference population (used only when type="HR")
n.legendre	number of nodes to approximate the cumulative hazard by Gauss-Legendre quadrature; default is 50
conf.int	numeric value giving the precision of the confidence intervals; default is 0.95
do.surv	If TRUE, the survival and its lower and upper confidence values are computed. Survival computation requires numerical integration and can be time-consuming so if you only want the hazard use do.surv=FALSE; default is TRUE
type,	if type="lpmatrix" returns the design matrix (or linear predictor matrix) corresponding to the new values of the covariates; if equals "HR", returns the predicted HR and CIs between newdata and newdata.ref; default is "standard" for classical hazard and survival estimation
exclude.random	if TRUE all random effects are set to zero; default is FALSE
get.deriv.H	if TRUE, the derivatives wrt to the regression parameters of the cumulative hazard are returned; default is FALSE
...	other arguments

Details

The confidence intervals noted CI.U are built on the log cumulative hazard scale U=log(H) (efficient scale in terms of respect towards the normality assumption) using Delta method. The confidence intervals on the survival scale are then CI.surv = exp(-exp(CI.U))

Value

List of objects:

haz	hazard predicted by the model
haz.inf	lower value for the confidence interval on the hazard based on the Bayesian covariance matrix Vp (Wood et al. 2016)
haz.sup	Upper value for the confidence interval on the hazard based on the Bayesian covariance matrix Vp
surv	survival predicted by the model
surv.inf	lower value for the confidence interval on the survival based on the Bayesian covariance matrix Vp
surv.sup	Upper value for the confidence interval on the survival based on the Bayesian covariance matrix Vp
deriv.H	derivatives wrt to the regression parameters of the cumulative hazard. Useful to calculate standardized survival
HR	predicted hazard ratio ; only when type = "HR"
HR.inf	lower value for the confidence interval on the hazard ratio based on the Bayesian covariance matrix Vp ; only when type = "HR"
HR.sup	Upper value for the confidence interval on the hazard ratio based on the Bayesian covariance matrix Vp ; only when type = "HR"

References

Wood, S.N., Pya, N. and Saefken, B. (2016), Smoothing parameter and model selection for general smooth models (with discussion). Journal of the American Statistical Association 111, 1548-1575

Examples

library(survPen)
data(datCancer) # simulated dataset with 2000 individuals diagnosed with cervical cancer

f1 <- ~tensor(fu,age,df=c(5,5))

# hazard model
mod1 <- survPen(f1,data=datCancer,t1=fu,event=dead,expected=NULL,method="LAML")

# predicting hazard and survival curves for age 60
nt <- seq(0,5,le=50)
pred <- predict(mod1,data.frame(fu=nt,age=60))
pred$haz
pred$surv

# predicting hazard ratio at 1 year according to age (with reference age of 50)
newdata1 <- data.frame(fu=1,age=seq(30,90,by=1))
newdata.ref1 <- data.frame(fu=1,age=rep(50,times=61))
predHR_1 <- predict(mod1,newdata=newdata1,newdata.ref=newdata.ref1,type="HR")
predHR_1$HR
predHR_1$HR.inf
predHR_1$HR.sup

# predicting hazard ratio at 3 years according to age (with reference age of 50)
newdata3 <- data.frame(fu=3,age=seq(30,90,by=1))
newdata.ref3 <- data.frame(fu=3,age=rep(50,times=61))
predHR_3 <- predict(mod1,newdata=newdata3,newdata.ref=newdata.ref3,type="HR")
predHR_3$HR
predHR_3$HR.inf
predHR_3$HR.sup

survPen documentation built on Sept. 14, 2023, 1:06 a.m.