riskRegression (version 1.3.7)

ate: Compute the average treatment effect using CSC.

Description

Use the g-formula to estimate the average treatment effect

Usage

ate(object, data, treatment, contrasts = NULL, times, cause,
  conf.level = 0.95, B = 0, seed, handler = c("mclapply", "foreach"),
  mc.cores = 1, verbose = TRUE, ...)

Arguments

object

outcome model which describes how event risk depends on treatment and covariates. The object carry its own call and have a predictRisk method. See examples.

data

data set in which to evaluate risk predictions based on the outcome model

treatment

name of the treatment variable

contrasts

the levels of treatment variable to be compared

times

time points at which to evaluate risks

cause

the cause of interest

conf.level

Numeric. Confidence level of the confidence intervals. Disregarded if negative.

B

the number of bootstrap replications used to compute the confidence intervals. If it equals 0, then Wald-type confidence intervals are computed. They rely on the standard error estimated using the influence function of the estimator.

seed

An integer used to generate seeds for bootstrap and to achieve reproducibility of the bootstrap confidence intervals.

handler

parallel handler for bootstrap. Either "mclapply" or "foreach". If "foreach" use doParallel to create a cluster.

mc.cores

Passed to parallel::mclapply or doParallel::registerDoParallel. The number of cores to use, i.e. at most how many child processes will be run simultaneously. The option is initialized from environment variable MC_CORES if set.

verbose

Logical. If TRUE inform about estimated run time.

...

passed to predictRisk

Value

A list with: point estimates, bootstrap quantile confidence intervals model: the CSC model (optional)

Details

WARNING: the p.value and the confidence intervals for the ratio using Wald-type approximations are still experimental.

Examples

library(survival)
library(rms)

set.seed(10)
n <- 1e2

## Cox model
dtS <- sampleData(n,outcome="survival")
dtS$time <- round(dtS$time,1)
dtS$X1 <- factor(rbinom(n, prob = c(0.3,0.4) , size = 2), labels = paste0("T",0:2))

fit=cph(formula = Surv(time,event)~ X1+X2,data=dtS,y=TRUE,x=TRUE)
## the cph object carries its call:
fit$call
## and there is a predictRisk method
"predictRisk.cph" %in% methods("predictRisk")

ateFit=ate(fit, data = dtS, treatment = "X1", contrasts = NULL,
        times = 5:7, B = 3, y = TRUE, mc.cores=1)

ateFit=ate(fit, data = dtS, treatment = "X1", contrasts = NULL,
        times = 5:7, B = 0, y = TRUE, mc.cores=1)

## Cause specific cox model
set.seed(17)
n=200
dt <- sampleData(n,outcome="competing.risks")
dt$time <- round(dt$time,1)
dt$X1 <- factor(rbinom(n, prob = c(0.2,0.3,0.2) , size = 3), labels = paste0("T",0:3))
fitCR= CSC(Hist(time,event)~ X1+X8,data=dt,cause=1)
ate(fitCR, data = dt, treatment = "X1", contrasts = NULL,
        times = 7, cause = 1, B = 2, mc.cores=1)

atefit=ate(fitCR, data = dt, treatment = "X1", contrasts = NULL,
        times = 1:7, cause = 1, B = 0, mc.cores=1,conf.level=FALSE)


 ate(fitCR, data = dt, treatment = "X1", contrasts = NULL,
        times = 5:7, cause = 1, B = 0, mc.cores=1)