Fwindow {dynpred} | R Documentation |
Calculate dynamic "death within window" curve, in other words, one minus fixed width conditional survival curves, defined as P(T<=t+w|T>t), for a fixed window width w.
Fwindow(object, width, variance = TRUE, conf.level = 0.95)
object |
|
width |
Width of the window |
variance |
Boolean (default= |
conf.level |
The confidence level, between 0 and 1 (default=0.95) |
"Die within window function" with window w, Fw(t) = P(T<=t+w|T>t), evaluated
at all time points t where the estimate changes value, and associated
pointwise confidence intervals (if variance
=TRUE
).
Both estimate and pointwise lower and upper confidence intervals are based on the negative exponential of the Nelson-Aalen estimate of the cumulative hazard, so P(T<=t+w|T>t) is estimated as exp(- int_t^t+w hatH_NA(s) ds), with hatH_NA the non-parametric Nelson-Aalen estimate.
Note: in object
, no event time points at or below zero allowed
A data frame with columns
time |
The time points t at which Fw(t) changes value (either t or t+width is an event time point) |
Fw |
The Fw(t) function |
low |
Lower end of confidence interval |
up |
Upper end of confidence interval |
and with attribute
"width"
as given as input.
Hein Putter H.Putter@lumc.nl
van Houwelingen HC, Putter H (2012). Dynamic Prediction in Clinical Survival Analysis. Chapman & Hall.
data(wbc1) c0 <- coxph(Surv(tyears, d) ~ 1, data = wbc1, method="breslow") sf0 <- survfit(c0) Fw <- Fwindow(sf0,4)