Nothing
library(smcfcs)
library(survival)
context("Cox proportional hazards model testing")
test_that("Cox imputation is approximately unbiased", {
skip_on_cran()
expect_equal({
set.seed(1234)
n <- 10000
z <- rnorm(n)
x <- z+rnorm(n)
t <- -log(runif(n))/(1*exp(x+z))
d <- 1*(t<10)
t[d==0] <- 10
x[(runif(n)<0.5)] <- NA
simData <- data.frame(t,d,x,z)
imps <- smcfcs(simData, smtype="coxph", smformula="Surv(t, d)~x+z",
method=c("", "", "norm", ""))
library(mitools)
impobj <- imputationList(imps$impDatasets)
models <- with(impobj, coxph(Surv(t,d)~x+z))
abs(summary(MIcombine(models))[1,1]-1)<0.1
}, TRUE)
})
test_that("Cox imputation works with only one covariate", {
skip_on_cran()
expect_equal({
set.seed(1234)
n <- 10000
x <- rnorm(n)
t <- -log(runif(n))/(0.01*exp(x))
d <- 1*(t<10)
t[d==0] <- 10
x[(runif(n)<0.5)] <- NA
simData <- data.frame(t,d,x)
imps <- smcfcs(simData, smtype="coxph", smformula="Surv(t, d)~x",
method=c("", "", "norm"))
library(mitools)
impobj <- imputationList(imps$impDatasets)
models <- with(impobj, coxph(Surv(t,d)~x))
abs(summary(MIcombine(models))[1,1]-1)<0.1
}, TRUE)
})
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