riskRegression (version 2020.02.05)

confint.ate: Confidence Intervals and Confidence Bands for the Predicted Absolute Risk (Cumulative Incidence Function)

Description

Confidence intervals and confidence Bands for the predicted absolute risk (cumulative incidence function).

Usage

# S3 method for ate
confint(
  object,
  parm = NULL,
  level = 0.95,
  nsim.band = 10000,
  meanRisk.transform = "none",
  diffRisk.transform = "none",
  ratioRisk.transform = "none",
  seed = NA,
  bootci.method = "perc",
  ...
)

Arguments

object

A ate object, i.e. output of the ate function.

parm

not used. For compatibility with the generic method.

level

[numeric, 0-1] Level of confidence.

nsim.band

[integer, >0]the number of simulations used to compute the quantiles for the confidence bands.

meanRisk.transform

[character] the transformation used to improve coverage of the confidence intervals for the mean risk in small samples. Can be "none", "log", "loglog", "cloglog".

diffRisk.transform

[character] the transformation used to improve coverage of the confidence intervals for the risk difference in small samples. Can be "none", "atanh".

ratioRisk.transform

[character] the transformation used to improve coverage of the confidence intervals for the risk ratio in small samples. Can be "none", "log".

seed

[integer, >0] seed number set when performing simulation for the confidence bands. If not given or NA no seed is set.

bootci.method

[character] Method for constructing bootstrap confidence intervals. Either "perc" (the default), "norm", "basic", "stud", or "bca".

...

not used.

Details

Confidence bands and confidence intervals computed via the influence function are automatically restricted to the interval [0;1].

Confidence intervals obtained via bootstrap are computed using the boot.ci function of the boot package. p-value are obtained using test inversion method (finding the smallest confidence level such that the interval contain the null hypothesis).

Examples

# NOT RUN {
library(survival)
library(data.table)

## ## generate data ####
set.seed(10)
d <- sampleData(70,outcome="survival")
d[, X1 := paste0("T",rbinom(.N, size = 2, prob = c(0.51)))]
## table(d$X1)

#### stratified Cox model ####
fit <- coxph(Surv(time,event)~X1 + strata(X2) + X6,
             data=d, ties="breslow", x = TRUE, y = TRUE)

#### average treatment effect ####
fit.ate <- ate(fit, treatment = "X1", times = 1:3, data = d,
               se = TRUE, iid = TRUE, band = TRUE)
print(fit.ate, type = "meanRisk")
dt.ate <- as.data.table(fit.ate)

## manual calculation of se
dd <- copy(d)
dd$X1 <- rep(factor("T0", levels = paste0("T",0:2)), NROW(dd))
out <- predictCox(fit, newdata = dd, se = TRUE, times = 1:3, average.iid = TRUE)
term1 <- -out$survival.average.iid
term2 <- sweep(1-out$survival, MARGIN = 2, FUN = "-", STATS = colMeans(1-out$survival))
sqrt(colSums((term1 + term2/NROW(d))^2)) 
## fit.ate$meanRisk[treatment=="T0",meanRisk.se]

## note
out2 <- predictCox(fit, newdata = dd, se = TRUE, times = 1:3, iid = TRUE)
mean(out2$survival.iid[,1,1])
out$survival.average.iid[1,1]

## check confidence intervals (no transformation)
dt.ate[,.(lower = pmax(0,value + qnorm(0.025) * se),
          lower2 = lower,
          upper = value + qnorm(0.975) * se,
          upper2 = upper)]

## add confidence intervals computed on the log-log scale
## and backtransformed
outCI <- confint(fit.ate,
                 meanRisk.transform = "loglog", diffRisk.transform = "atanh",
                 ratioRisk.transform = "log")
print(outCI, type = "meanRisk")

dt.ate[type == "ate", newse := se/(value*log(value))]
dt.ate[type == "ate", .(lower = exp(-exp(log(-log(value)) - 1.96 * newse)),
                        upper = exp(-exp(log(-log(value)) + 1.96 * newse)))]
# }