Confidence Intervals and Confidence Bands for the predicted Survival/Cumulative Hazard
Confidence intervals and confidence Bands for the predicted survival/cumulative Hazard.
## S3 method for class 'predictCox' confint( object, parm = NULL, level = 0.95, n.sim = 10000, cumhazard.transform = "log", survival.transform = "loglog", seed = NA, ... )
object |
A |
parm |
[character] the type of predicted value for which the confidence intervals should be output.
Can be |
level |
[numeric, 0-1] Level of confidence. |
n.sim |
[integer, >0] the number of simulations used to compute the quantiles for the confidence bands. |
cumhazard.transform |
[character] the transformation used to improve coverage
of the confidence intervals for the cumlative hazard in small samples.
Can be |
survival.transform |
[character] the transformation used to improve coverage
of the confidence intervals for the survival in small samples.
Can be |
seed |
[integer, >0] seed number set before performing simulations for the confidence bands. If not given or NA no seed is set. |
... |
not used. |
The confidence bands and confidence intervals are automatically restricted to the interval of definition of the statistic, i.e. a confidence interval for the survival of [0.5;1.2] will become [0.5;1].
Brice Ozenne
library(survival)
#### generate data ####
set.seed(10)
d <- sampleData(40,outcome="survival")
#### estimate a stratified Cox model ####
fit <- coxph(Surv(time,event)~X1 + strata(X2) + X6,
data=d, ties="breslow", x = TRUE, y = TRUE)
#### compute individual specific survival probabilities
fit.pred <- predictCox(fit, newdata=d[1:3], times=c(3,8), type = "survival",
se = TRUE, iid = TRUE, band = TRUE)
fit.pred
## check standard error
sqrt(rowSums(fit.pred$survival.iid[,,1]^2)) ## se for individual 1
## check confidence interval
newse <- fit.pred$survival.se/(-fit.pred$survival*log(fit.pred$survival))
cbind(lower = as.double(exp(-exp(log(-log(fit.pred$survival)) + 1.96 * newse))),
upper = as.double(exp(-exp(log(-log(fit.pred$survival)) - 1.96 * newse)))
)
#### compute confidence intervals without transformation
confint(fit.pred, survival.transform = "none")
cbind(lower = as.double(fit.pred$survival - 1.96 * fit.pred$survival.se),
upper = as.double(fit.pred$survival + 1.96 * fit.pred$survival.se)
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