survPen: predict.survPen – R documentation

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predict.survPen

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

# model : unidimensional penalized spline for time since diagnosis with 5 knots
f1 <- ~smf(fu,df=5)

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

# predicting hazard and survival at time 1
pred <- predict(mod1,data.frame(fu=1))
pred$haz
pred$surv

# predicting hazard ratio between age 70 and age 30
pred.HR <- predict(mod1,data.frame(fu=1,age=70),newdata.ref=data.frame(fu=1,age=30),type="HR")
pred.HR$HR
pred.HR$HR.inf
pred.HR$HR.sup

survPen

Multidimensional Penalized Splines for Survival and Net Survival Models

v1.5.1

GPL-3 | file LICENSE

Authors

Mathieu Fauvernier [aut, cre], Laurent Roche [aut], Laurent Remontet [aut], Zoe Uhry [ctb], Nadine Bossard [ctb]

Initial release