Nothing
### test-BuyseTest-tableComparison.R ---
##----------------------------------------------------------------------
## Author: Brice Ozenne
## Created: maj 26 2018 (14:33)
## Version:
## Last-Updated: May 1 2023 (10:07)
## By: Brice Ozenne
## Update #: 71
##----------------------------------------------------------------------
##
### Commentary:
##
### Change Log:
##----------------------------------------------------------------------
##
### Code:
if(FALSE){
library(testthat)
library(BuyseTest)
library(data.table)
}
context("Check tableComparison matches the summary of BuyseTest objects")
## * Settings
n.patients <- c(90,100)
BuyseTest.options(check = TRUE,
keep.pairScore = TRUE,
keep.survival = TRUE,
method.inference = "none",
pool.strata = "Buyse",
trace = 0)
## * Simulated data
set.seed(10)
dt.sim <- simBuyseTest(n.T = n.patients[1],
n.C = n.patients[2],
argsBin = list(p.T = list(c(0.5,0.5),c(0.25,0.75))),
argsCont = list(mu.T = 1:3, sigma.T = rep(1,3)),
argsTTE = list(scale.T = 1:3, scale.censoring.T = rep(1,3)))
dt.sim[eventtime1 >= 1, status1 := 0]
dt.sim[, time1 := eventtime1]
dt.sim[eventtime1 >= 1, time1 := 1]
## * test against tableComparison (no correction)
formula <- treatment ~ tte(time1, status1, threshold = 0.5) + cont(score1, 1) + bin(toxicity1) + tte(time1, status1, threshold = 0.25) + cont(score1, 0.5)
test_that("Full data - no correction", {
BT.mixed <- BuyseTest(formula, data = dt.sim, scoring.rule = "Peron", correction.uninf = FALSE)
expect_equal(as.double(BT.mixed@n.pairs),
prod(table(dt.sim$treatment)))
manualScore <- NULL
for(iEndpoint in 1:length(BT.mixed@endpoint)){ ## iEndpoint <- 1
iScore <- getPairScore(BT.mixed, endpoint = iEndpoint)[,.(favorable = sum(favorable*weight),
unfavorable = sum(unfavorable*weight),
neutral = sum(neutral*weight),
uninf = sum(uninf*weight))]
manualScore <- rbind(manualScore,iScore)
}
## check tablePairScore
expect_equal(as.double(manualScore$favorable),
as.double(coef(BT.mixed, statistic = "count.favorable", cumulative = FALSE)))
expect_equal(as.double(manualScore$unfavorable),
as.double(coef(BT.mixed, statistic = "count.unfavorable", cumulative = FALSE)))
expect_equal(as.double(manualScore$neutral),
as.double(coef(BT.mixed, statistic = "count.neutral", cumulative = FALSE)))
expect_equal(as.double(manualScore$uninf),
as.double(coef(BT.mixed, statistic = "count.uninf", cumulative = FALSE)))
expect_equal(as.double(cumsum(BT.mixed@count.favorable-BT.mixed@count.unfavorable)/BT.mixed@n.pairs),
as.double(coef(BT.mixed, statistic = "netBenefit")))
expect_equal(as.double(cumsum(BT.mixed@count.favorable)/cumsum(BT.mixed@count.unfavorable)),
as.double(coef(BT.mixed, statistic = "winRatio")))
## check number of pairs
D <- length(BT.mixed@endpoint)
vec.pair <- (coef(BT.mixed, statistic = "count.favorable", cumulative = FALSE) + coef(BT.mixed, statistic = "count.unfavorable", cumulative = FALSE) + coef(BT.mixed, statistic = "count.neutral", cumulative = FALSE) + coef(BT.mixed, statistic = "count.uninf", cumulative = FALSE))
vec.RP <- (coef(BT.mixed, statistic = "count.neutral", cumulative = FALSE) + coef(BT.mixed, statistic = "count.uninf", cumulative = FALSE))
expect_equal(as.double(vec.RP[-D]),as.double(vec.pair[-1]))
})
## * test against tableComparison (correction)
formula <- treatment ~ tte(time1, status1, threshold = 0.5) + cont(score1, 1) + bin(toxicity1) + tte(time1, status1, threshold = 0.25) + cont(score1, 0.5)
test_that("Full data", {
BT.mixed <- suppressWarnings(BuyseTest(formula, data = dt.sim, scoring.rule = "Peron", correction.uninf = TRUE))
expect_equal(as.double(BT.mixed@n.pairs),
prod(table(dt.sim$treatment)))
manualScore <- NULL
for(iEndpoint in 1:length(BT.mixed@endpoint)){ ## iEndpoint <- 1
iScore <- getPairScore(BT.mixed, endpoint = iEndpoint)[,.(favorable = sum(favorableC),
unfavorable = sum(unfavorableC),
neutral = sum(neutralC))]
manualScore <- rbind(manualScore,iScore)
}
## check tablePairScore
expect_equal(unname(coef(BT.mixed, statistic = "netBenefit")),manualScore[,cumsum(favorable-unfavorable)]/BT.mixed@n.pairs)
expect_equal(unname(coef(BT.mixed, statistic = "winRatio")),manualScore[,cumsum(favorable)/cumsum(unfavorable)])
## check number of pairs
D <- length(BT.mixed@endpoint)
vec.pair <- (coef(BT.mixed, statistic = "count.favorable", cumulative = FALSE) + coef(BT.mixed, statistic = "count.unfavorable", cumulative = FALSE) + coef(BT.mixed, statistic = "count.neutral", cumulative = FALSE) + coef(BT.mixed, statistic = "count.uninf", cumulative = FALSE))
vec.RP <- (coef(BT.mixed, statistic = "count.neutral", cumulative = FALSE) + coef(BT.mixed, statistic = "count.uninf", cumulative = FALSE))
expect_equal(as.double(vec.RP[-D]),as.double(vec.pair[-1]))
})
##----------------------------------------------------------------------
### test-BuyseTest-tableComparison.R ends here
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.