tests/testthat/test_IntegralScores.R

In adoptr: Adaptive Optimal Two-Stage Designs

context("Test integral scores")

null        <- PointMassPrior(.0, 1)
alternative <- PointMassPrior(.4, 1)
dist        <- Normal()

# Define design from rpact
design_rp2 <- rpact::getDesignInverseNormal(
    kMax = 2, alpha = 0.05, beta = 0.2, futilityBounds = 0, typeOfDesign = "P")

res  <- rpact::getSampleSizeMeans(
    design_rp2, normalApproximation = TRUE, alternative = .4)
char <- rpact::getDesignCharacteristics(design_rp2)
n1   <- res$numberOfSubjects1[1, 1]
n2   <- res$numberOfSubjects1[2, 1] - n1
c1f  <- qnorm(char$futilityProbabilities) +
    sqrt(res$numberOfSubjects1[1]) * .4 / sqrt(2)
c1e  <- design_rp2$criticalValues[1]

f <- function(z){
    w1 <- 1 / sqrt(2)
    w2 <- sqrt(1 - w1^2)
    out <- (design_rp2$criticalValues[2] - w1 * z) / w2
    return(out)
}

x <- adoptr:::GaussLegendreRule(5)$nodes
h <- (c1e - c1f) / 2
x <- h * x + (h + c1f)

design_gs <- TwoStageDesign(
    n1,
    c1f,
    c1e,
    rep(n2, 5),
    sapply(x, f))


# Simulation
sim_alt  <- simulate(
    design_gs, nsim = 1e4, dist = Normal(), theta = .4, seed = 59)

sim_null <- simulate(
    design_gs, nsim = 1e4, dist = Normal(), theta = .0, seed = 59)


test_that("Expected sample size is computed correctly",{

    # optimization = TRUE uses non-rounded values of n (as does rpact!)

    # Expected Sample sizes under H1
    expect_equal(
        res$expectedNumberOfSubjectsH1/2, # per group!
        evaluate(ExpectedSampleSize(dist, alternative), design_gs, optimization = TRUE),
        tolerance = .1, scale = 1)

    expect_equal(
        res$expectedNumberOfSubjectsH1/2,
        evaluate(ExpectedSampleSize(dist, alternative),
                   design_gs, specific = FALSE, optimization = TRUE),
        tolerance = .1, scale = 1)

    # Expected Sample sizes under H0
    expect_equal(
        res$expectedNumberOfSubjectsH1/2,
        evaluate(expected(ConditionalSampleSize(), dist, null), design_gs, optimization = TRUE),
        tolerance = .1, scale = 1)

    expect_equal(
        res$expectedNumberOfSubjectsH0/2,
        evaluate(expected(ConditionalSampleSize(), dist, null),
                 design_gs, specific = FALSE, optimization = TRUE),
        tolerance = .1, scale = 1)

}) # end 'expected sample size is computed correctly'



test_that("Power is computed correctly for example design", {

    pow <- Power(Normal(), PointMassPrior(.4, 1))

    expect_equal(
        evaluate(pow, design_gs),
        .8,
        tolerance = 1e-2, scale = 1)

    expect_equal(
        evaluate(pow, design_gs, specific = FALSE),
        .8,
        tolerance = 1e-2, scale = 1)

    expect_equal(
        mean(sim_alt[, "reject"]),
        .8,
        tolerance = 1e-2, scale = 1)

}) # end 'power is computed correctly for example design'



test_that("Type one error is computed correctly for example design", {

    toer <- Power(Normal(), PointMassPrior(.0, 1))
    expect_equal(
        evaluate(toer, design_gs),
        .05,
        tolerance = 1e-3, scale = 1)

    expect_equal(
        evaluate(toer, design_gs, specific = FALSE),
        .05,
        tolerance = 1e-3, scale = 1)

    expect_equal(
        mean(sim_null[, "reject"]),
        .05,
        tolerance = 1e-3, scale = 1)

}) # end 'type one error is computed correctly for example design'