pew: Calculate dynamic prediction error curve

In dynpred: Companion Package to "Dynamic Prediction in Clinical Survival Analysis"

Description

Calculate dynamic fixed width prediction error curve.

Usage

pew(time, status, tsurv, survmat, tcens, censmat, width, FUN = c("KL",
  "Brier"), tout)

pewcox(formula, censformula, width, data, censdata, FUN = c("KL", "Brier"),
  tout, CV = FALSE, progress = FALSE)

Arguments

time	Vector of time points in data
status	Vector of event indicators in data
tsurv	Vector of time points corresponding to the estimated survival probabilities in survmat
survmat	Matrix of estimated survival probabilities; dimension should be length of tsurv x length of time
tcens	Vector of time points corresponding to the estimated censoring probabilities in censmat
censmat	Matrix of estimated censoring probabilities; dimension should be length of tcens x length of time
width	Width of the window
FUN	The error function, either "KL" (default) for Kullback-Leibler or "Brier" for Brier score
tout	Vector of time points at which to evaluate prediction error. If missing, prediction error will be evaluated at all time points where the estimate will change value
formula	Formula for prediction model to be used as in coxph
censformula	Formula for censoring model, also to be used as in coxph
data	Data set in which to interpret formula
censdata	Data set in which to interpret censformula
CV	Boolean (default=FALSE); if TRUE, (leave-one-out) cross-validation is used for the survival probabilities
progress	Boolean (default=FALSE); if TRUE, progress is printed on screen

Details

Corresponds to Equation (3.6) in van Houwelingen and Putter (2011). The censformula is used to calculate inverse probability of censoring weights (IPCW).

Value

A data frame with columns

time	Event time points
Err	Prediction error of model specified by formula at these time points

and with attribute "width" given as input.

Author(s)

Hein Putter [e-mail]

References

van Houwelingen HC, Putter H (2012). Dynamic Prediction in Clinical Survival Analysis. Chapman & Hall.

Examples

data(ova)
# Example on a subset, because the effect of CV is clearer
ova2 <- ova[1:100,]
pewcox(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, Surv(tyears, 1-d) ~ 1,
  width=2, data = ova2, FUN="Brier", tout=seq(0,6,by=0.5))
pewcox(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, Surv(tyears, 1-d) ~ 1,
  width=2, data = ova2, FUN="Brier", tout=seq(0,6,by=0.5), CV=TRUE, progress=TRUE)


pewcox(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, Surv(tyears, 1-d) ~ 1,
  width=2, data = ova, FUN="Brier", tout=seq(0,6,by=0.5))
pewcox(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, Surv(tyears, 1-d) ~ 1,
  width=2, data = ova, FUN="Brier", tout=seq(0,6,by=0.5), CV=TRUE, progress=TRUE)

dynpred documentation built on May 2, 2019, 5:07 a.m.