confint.predictCox: Confidence Intervals and Confidence Bands for the predicted Survival/Cumulative Hazard

Description

Confidence intervals and confidence Bands for the predicted survival/cumulative Hazard.

Usage

# S3 method for predictCox
confint(
  object,
  parm = NULL,
  level = 0.95,
  n.sim = 10000,
  cumhazard.transform = "log",
  survival.transform = "loglog",
  seed = NA,
  ...
)

Arguments

object: A predictCox object, i.e. output of the predictCox function.
parm: [character] the type of predicted value for which the confidence intervals should be output. Can be "survival" or "cumhazard".
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 "none", "log".
survival.transform: [character] the transformation used to improve coverage of the confidence intervals for the survival in small samples. Can be "none", "log", "loglog", "cloglog".
seed: [integer, >0] seed number set before performing simulations for the confidence bands. If not given or NA no seed is set.
...: not used.

Author

Brice Ozenne

Details

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].

Examples

Run this code

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)
)