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

 ```1 2 3 4 5``` ```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 H.Putter@lumc.nl

References

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

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```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.