Nothing
tests/testthat/test-km-power-samplesize.R
In lrstat: Power and Sample Size Calculation for Non-Proportional Hazards and Beyond
testthat::test_that("kmpower and kmpower1s: power increases with sample size and effect size", {
km <- kmpower(
kMax = 2,
informationRates = c(0.8, 1),
alpha = 0.025,
typeAlphaSpending = "sfOF",
milestone = 18,
allocationRatioPlanned = 1,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda1 = c(0.0533, 0.0309, 1.5 * 0.0533, 1.5 * 0.0309),
lambda2 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
gamma1 = -log(1 - 0.05) / 12,
gamma2 = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = 18,
fixedFollowup = FALSE
)
km_big <- kmpower(
kMax = 2,
informationRates = c(0.8, 1),
alpha = 0.025,
typeAlphaSpending = "sfOF",
milestone = 18,
allocationRatioPlanned = 1,
accrualTime = seq(0, 8),
accrualIntensity = 2 * (26 / 9 * seq(1, 9)),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda1 = c(0.0533, 0.0309, 1.5 * 0.0533, 1.5 * 0.0309),
lambda2 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
gamma1 = -log(1 - 0.05) / 12,
gamma2 = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = 18,
fixedFollowup = FALSE
)
km_weaker <- kmpower(
kMax = 2,
informationRates = c(0.8, 1),
alpha = 0.025,
typeAlphaSpending = "sfOF",
milestone = 18,
allocationRatioPlanned = 1,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda1 = c(0.0533, 0.0450, 1.5 * 0.0533, 1.5 * 0.0450),
lambda2 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
gamma1 = -log(1 - 0.05) / 12,
gamma2 = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = 18,
fixedFollowup = FALSE
)
km1s <- kmpower1s(
kMax = 2,
informationRates = c(0.8, 1),
alpha = 0.025,
typeAlphaSpending = "sfOF",
milestone = 18,
survH0 = 0.30,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda = c(0.0533, 0.0309, 1.5 * 0.0533, 1.5 * 0.0309),
gamma = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = 18,
fixedFollowup = FALSE
)
km1s_big <- kmpower1s(
kMax = 2,
informationRates = c(0.8, 1),
alpha = 0.025,
typeAlphaSpending = "sfOF",
milestone = 18,
survH0 = 0.30,
accrualTime = seq(0, 8),
accrualIntensity = 2 * (26 / 9 * seq(1, 9)),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda = c(0.0533, 0.0309, 1.5 * 0.0533, 1.5 * 0.0309),
gamma = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = 18,
fixedFollowup = FALSE
)
testthat::expect_s3_class(km, "kmpower")
testthat::expect_named(km, c("byStageResults", "overallResults", "settings", "byTreatmentCounts"))
testthat::expect_equal(round(km$overallResults$overallReject, 4), 0.7634)
testthat::expect_equal(km$overallResults$numberOfSubjects, 468)
testthat::expect_equal(km$overallResults$studyDuration, 40)
testthat::expect_true(km_big$overallResults$overallReject > km$overallResults$overallReject)
testthat::expect_true(km$overallResults$overallReject > km_weaker$overallResults$overallReject)
testthat::expect_true(km1s_big$overallResults$overallReject > km1s$overallResults$overallReject)
testthat::expect_s3_class(km1s, "kmpower1s")
testthat::expect_named(km1s, c("byStageResults", "overallResults", "settings"))
testthat::expect_equal(round(km1s$overallResults$overallReject, 4), 0.9557)
})
testthat::test_that("kmsamplesize family: solved designs invert target power within tolerance", {
ss <- kmsamplesize(
beta = 0.25,
kMax = 2,
informationRates = c(0.8, 1),
alpha = 0.025,
typeAlphaSpending = "sfOF",
milestone = 18,
allocationRatioPlanned = 1,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda1 = c(0.0533, 0.0309, 1.5 * 0.0533, 1.5 * 0.0309),
lambda2 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
gamma1 = -log(1 - 0.05) / 12,
gamma2 = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = NA,
fixedFollowup = FALSE
)
ss1s <- kmsamplesize1s(
beta = 0.2,
kMax = 2,
informationRates = c(0.8, 1),
alpha = 0.025,
typeAlphaSpending = "sfOF",
milestone = 18,
survH0 = 0.30,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda = c(0.0533, 0.0309, 1.5 * 0.0533, 1.5 * 0.0309),
gamma = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = NA,
fixedFollowup = FALSE
)
ss_eq <- kmsamplesizeequiv(
beta = 0.1,
kMax = 2,
informationRates = c(0.5, 1),
alpha = 0.05,
typeAlphaSpending = "sfOF",
milestone = 18,
survDiffLower = -0.13,
survDiffUpper = 0.13,
allocationRatioPlanned = 1,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda1 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
lambda2 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
gamma1 = -log(1 - 0.05) / 12,
gamma2 = -log(1 - 0.05) / 12,
accrualDuration = NA,
followupTime = 18,
fixedFollowup = FALSE
)
testthat::expect_named(ss, c("resultsUnderH1", "resultsUnderH0"))
testthat::expect_s3_class(ss$resultsUnderH1, "kmpower")
testthat::expect_equal(round(ss$resultsUnderH1$overallResults$overallReject, 4), 0.7500)
testthat::expect_equal(round(ss$resultsUnderH0$overallResults$overallReject, 4), 0.0250)
testthat::expect_equal(round(ss$resultsUnderH1$overallResults$followupTime, 4), 14.4496)
testthat::expect_equal(ss$resultsUnderH1$overallResults$numberOfSubjects, 468)
testthat::expect_named(ss1s, c("resultsUnderH1", "resultsUnderH0"))
testthat::expect_s3_class(ss1s$resultsUnderH1, "kmpower1s")
testthat::expect_equal(round(ss1s$resultsUnderH1$overallResults$overallReject, 4), 0.8000)
testthat::expect_equal(round(ss1s$resultsUnderH1$overallResults$followupTime, 4), 4.5323)
testthat::expect_equal(ss1s$resultsUnderH1$overallResults$numberOfSubjects, 468)
testthat::expect_s3_class(ss_eq, "kmpowerequiv")
testthat::expect_equal(round(ss_eq$overallResults$overallReject, 4), 0.9000)
testthat::expect_equal(round(ss_eq$overallResults$accrualDuration, 4), 23.9615)
testthat::expect_equal(round(ss_eq$overallResults$followupTime, 4), 17.6773)
testthat::expect_equal(ss_eq$overallResults$numberOfSubjects, 519)
})
testthat::test_that("one-sided and equivalence KM functions show expected directionality", {
km_eq <- kmpowerequiv(
kMax = 2,
informationRates = c(0.5, 1),
alpha = 0.05,
typeAlphaSpending = "sfOF",
milestone = 18,
survDiffLower = -0.13,
survDiffUpper = 0.13,
allocationRatioPlanned = 1,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda1 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
lambda2 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
gamma1 = -log(1 - 0.05) / 12,
gamma2 = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = 18,
fixedFollowup = FALSE
)
km_eq_off <- kmpowerequiv(
kMax = 2,
informationRates = c(0.5, 1),
alpha = 0.05,
typeAlphaSpending = "sfOF",
milestone = 18,
survDiffLower = -0.13,
survDiffUpper = 0.13,
allocationRatioPlanned = 1,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda1 = c(0.0533, 0.0309, 1.5 * 0.0533, 1.5 * 0.0309),
lambda2 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
gamma1 = -log(1 - 0.05) / 12,
gamma2 = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = 18,
fixedFollowup = FALSE
)
km_two_sided <- kmpower(
kMax = 2,
informationRates = c(0.8, 1),
alpha = 0.025,
typeAlphaSpending = "sfOF",
milestone = 18,
allocationRatioPlanned = 1,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda1 = c(0.0533, 0.0309, 1.5 * 0.0533, 1.5 * 0.0309),
lambda2 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
gamma1 = -log(1 - 0.05) / 12,
gamma2 = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = 18,
fixedFollowup = FALSE
)
km_one_sided <- kmpower1s(
kMax = 2,
informationRates = c(0.8, 1),
alpha = 0.025,
typeAlphaSpending = "sfOF",
milestone = 18,
survH0 = 0.30,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda = c(0.0533, 0.0309, 1.5 * 0.0533, 1.5 * 0.0309),
gamma = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = 18,
fixedFollowup = FALSE
)
testthat::expect_s3_class(km_eq, "kmpowerequiv")
testthat::expect_equal(round(km_eq$overallResults$overallReject, 4), 0.8609)
testthat::expect_true(km_eq$overallResults$overallReject > km_eq_off$overallResults$overallReject)
testthat::expect_true(km_one_sided$overallResults$overallReject > km_two_sided$overallResults$overallReject)
})
testthat::test_that("kmest: estimate and standard error", {
library(survival)
df1 <- kmest(aml, stratum="x", time="time", event="status",
conftype="none")
df2 <- summary(survfit(Surv(time, status) ~ x, data=aml,
conf.type="none"))
testthat::expect_equal(df1$surv[df1$time > 0], df2$surv)
testthat::expect_equal(df1$sesurv[df1$time > 0], df2$std.err)
})
testthat::test_that("kmest: confidence interval", {
library(survival)
for (conftype in c("plain", "log", "log-log", "arcsin")) {
df1 <- kmest(aml, stratum="x", time="time", event="status",
conftype=conftype)
df2 <- summary(survfit(Surv(time, status) ~ x, data=aml,
conf.type=conftype))
testthat::expect_equal(df1$lower[df1$time > 0], df2$lower)
testthat::expect_equal(df1$upper[df1$time > 0], df2$upper)
}
})
testthat::test_that("rmdiff: unstratified test of rmst difference", {
library(survival)
df1 <- rmdiff(veteran, treat="trt", time="time",
event="status", milestone=90)
# the following values are obtained from SAS PROC LIFETEST:
# proc lifetest data=veteran rmst(tau=90);
# time time*status(0);
# strata trt;
# run;
rmst1 = 62.75618
rmst2 = 56.45116
stderr1 = 4.0321
stderr2 = 3.9790
rmstdiffchisq = 1.2388
pvalue = 0.2657
testthat::expect_equal(c(round(df1$rmst1, 5),
round(df1$rmst2, 5),
round(sqrt(df1$vrmst1), 4),
round(sqrt(df1$vrmst2), 4),
round(df1$rmstDiffZ^2, 4),
round(df1$rmstDiffPValue, 4)),
c(rmst1, rmst2, stderr1, stderr2,
rmstdiffchisq, pvalue))
})
testthat::test_that("rmdiff: stratified test of rmst difference", {
library(survival)
df1 <- rmdiff(veteran, stratum="celltype", treat="trt",
time="time", event="status", milestone=90)
# Of note, the stratified results are different from SAS PROC LIFETEST:
# proc lifetest data=veteran rmst(tau=90);
# time time*status(0);
# strata celltype / group = trt;
# run;
# This is because SAS adds up the rmst diffs and variances across strata,
# while we use the number of subjects as the weight for each stratum.
# Our approach yields a more meaningful rmst diff across strata.
# To reproduce our results, we combine the stratum-specific information
# from SAS PROC LIFETEST using the sample size weights across strata
n = c(15, 20, 30, 18, 9, 18, 15, 12)
rmst = c(68.55152, 65.95, 50.86667, 45.16667, 56.44444, 51.91667,
84.8, 64.25)
stderr = c(8.3317, 7.5975, 5.7978, 7.8103, 12.7986, 6.9569,
5.0237, 8.0847)
a = c(1, 3, 5, 7)
b = c(2, 4, 6, 8)
ns = n[a] + n[b]
rmstDiffs = rmst[a] - rmst[b]
vrmstDiffs = stderr[a]^2 + stderr[b]^2
w = ns/sum(ns)
rmstDiff = sum(w*rmstDiffs)
sermstDiff = sqrt(sum(w*w*vrmstDiffs))
rmstDiffZ = rmstDiff/sermstDiff
testthat::expect_equal(c(round(df1$rmstDiff, 4),
round(df1$sermstDiff, 3),
round(df1$rmstDiffZ, 3)),
c(round(rmstDiff, 4),
round(sermstDiff, 3),
round(rmstDiffZ, 3)))
})
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testthat::test_that("kmpower and kmpower1s: power increases with sample size and effect size", {
km <- kmpower(
kMax = 2,
informationRates = c(0.8, 1),
alpha = 0.025,
typeAlphaSpending = "sfOF",
milestone = 18,
allocationRatioPlanned = 1,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda1 = c(0.0533, 0.0309, 1.5 * 0.0533, 1.5 * 0.0309),
lambda2 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
gamma1 = -log(1 - 0.05) / 12,
gamma2 = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = 18,
fixedFollowup = FALSE
)
km_big <- kmpower(
kMax = 2,
informationRates = c(0.8, 1),
alpha = 0.025,
typeAlphaSpending = "sfOF",
milestone = 18,
allocationRatioPlanned = 1,
accrualTime = seq(0, 8),
accrualIntensity = 2 * (26 / 9 * seq(1, 9)),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda1 = c(0.0533, 0.0309, 1.5 * 0.0533, 1.5 * 0.0309),
lambda2 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
gamma1 = -log(1 - 0.05) / 12,
gamma2 = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = 18,
fixedFollowup = FALSE
)
km_weaker <- kmpower(
kMax = 2,
informationRates = c(0.8, 1),
alpha = 0.025,
typeAlphaSpending = "sfOF",
milestone = 18,
allocationRatioPlanned = 1,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda1 = c(0.0533, 0.0450, 1.5 * 0.0533, 1.5 * 0.0450),
lambda2 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
gamma1 = -log(1 - 0.05) / 12,
gamma2 = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = 18,
fixedFollowup = FALSE
)
km1s <- kmpower1s(
kMax = 2,
informationRates = c(0.8, 1),
alpha = 0.025,
typeAlphaSpending = "sfOF",
milestone = 18,
survH0 = 0.30,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda = c(0.0533, 0.0309, 1.5 * 0.0533, 1.5 * 0.0309),
gamma = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = 18,
fixedFollowup = FALSE
)
km1s_big <- kmpower1s(
kMax = 2,
informationRates = c(0.8, 1),
alpha = 0.025,
typeAlphaSpending = "sfOF",
milestone = 18,
survH0 = 0.30,
accrualTime = seq(0, 8),
accrualIntensity = 2 * (26 / 9 * seq(1, 9)),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda = c(0.0533, 0.0309, 1.5 * 0.0533, 1.5 * 0.0309),
gamma = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = 18,
fixedFollowup = FALSE
)
testthat::expect_s3_class(km, "kmpower")
testthat::expect_named(km, c("byStageResults", "overallResults", "settings", "byTreatmentCounts"))
testthat::expect_equal(round(km$overallResults$overallReject, 4), 0.7634)
testthat::expect_equal(km$overallResults$numberOfSubjects, 468)
testthat::expect_equal(km$overallResults$studyDuration, 40)
testthat::expect_true(km_big$overallResults$overallReject > km$overallResults$overallReject)
testthat::expect_true(km$overallResults$overallReject > km_weaker$overallResults$overallReject)
testthat::expect_true(km1s_big$overallResults$overallReject > km1s$overallResults$overallReject)
testthat::expect_s3_class(km1s, "kmpower1s")
testthat::expect_named(km1s, c("byStageResults", "overallResults", "settings"))
testthat::expect_equal(round(km1s$overallResults$overallReject, 4), 0.9557)
})
testthat::test_that("kmsamplesize family: solved designs invert target power within tolerance", {
ss <- kmsamplesize(
beta = 0.25,
kMax = 2,
informationRates = c(0.8, 1),
alpha = 0.025,
typeAlphaSpending = "sfOF",
milestone = 18,
allocationRatioPlanned = 1,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda1 = c(0.0533, 0.0309, 1.5 * 0.0533, 1.5 * 0.0309),
lambda2 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
gamma1 = -log(1 - 0.05) / 12,
gamma2 = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = NA,
fixedFollowup = FALSE
)
ss1s <- kmsamplesize1s(
beta = 0.2,
kMax = 2,
informationRates = c(0.8, 1),
alpha = 0.025,
typeAlphaSpending = "sfOF",
milestone = 18,
survH0 = 0.30,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda = c(0.0533, 0.0309, 1.5 * 0.0533, 1.5 * 0.0309),
gamma = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = NA,
fixedFollowup = FALSE
)
ss_eq <- kmsamplesizeequiv(
beta = 0.1,
kMax = 2,
informationRates = c(0.5, 1),
alpha = 0.05,
typeAlphaSpending = "sfOF",
milestone = 18,
survDiffLower = -0.13,
survDiffUpper = 0.13,
allocationRatioPlanned = 1,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda1 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
lambda2 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
gamma1 = -log(1 - 0.05) / 12,
gamma2 = -log(1 - 0.05) / 12,
accrualDuration = NA,
followupTime = 18,
fixedFollowup = FALSE
)
testthat::expect_named(ss, c("resultsUnderH1", "resultsUnderH0"))
testthat::expect_s3_class(ss$resultsUnderH1, "kmpower")
testthat::expect_equal(round(ss$resultsUnderH1$overallResults$overallReject, 4), 0.7500)
testthat::expect_equal(round(ss$resultsUnderH0$overallResults$overallReject, 4), 0.0250)
testthat::expect_equal(round(ss$resultsUnderH1$overallResults$followupTime, 4), 14.4496)
testthat::expect_equal(ss$resultsUnderH1$overallResults$numberOfSubjects, 468)
testthat::expect_named(ss1s, c("resultsUnderH1", "resultsUnderH0"))
testthat::expect_s3_class(ss1s$resultsUnderH1, "kmpower1s")
testthat::expect_equal(round(ss1s$resultsUnderH1$overallResults$overallReject, 4), 0.8000)
testthat::expect_equal(round(ss1s$resultsUnderH1$overallResults$followupTime, 4), 4.5323)
testthat::expect_equal(ss1s$resultsUnderH1$overallResults$numberOfSubjects, 468)
testthat::expect_s3_class(ss_eq, "kmpowerequiv")
testthat::expect_equal(round(ss_eq$overallResults$overallReject, 4), 0.9000)
testthat::expect_equal(round(ss_eq$overallResults$accrualDuration, 4), 23.9615)
testthat::expect_equal(round(ss_eq$overallResults$followupTime, 4), 17.6773)
testthat::expect_equal(ss_eq$overallResults$numberOfSubjects, 519)
})
testthat::test_that("one-sided and equivalence KM functions show expected directionality", {
km_eq <- kmpowerequiv(
kMax = 2,
informationRates = c(0.5, 1),
alpha = 0.05,
typeAlphaSpending = "sfOF",
milestone = 18,
survDiffLower = -0.13,
survDiffUpper = 0.13,
allocationRatioPlanned = 1,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda1 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
lambda2 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
gamma1 = -log(1 - 0.05) / 12,
gamma2 = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = 18,
fixedFollowup = FALSE
)
km_eq_off <- kmpowerequiv(
kMax = 2,
informationRates = c(0.5, 1),
alpha = 0.05,
typeAlphaSpending = "sfOF",
milestone = 18,
survDiffLower = -0.13,
survDiffUpper = 0.13,
allocationRatioPlanned = 1,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda1 = c(0.0533, 0.0309, 1.5 * 0.0533, 1.5 * 0.0309),
lambda2 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
gamma1 = -log(1 - 0.05) / 12,
gamma2 = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = 18,
fixedFollowup = FALSE
)
km_two_sided <- kmpower(
kMax = 2,
informationRates = c(0.8, 1),
alpha = 0.025,
typeAlphaSpending = "sfOF",
milestone = 18,
allocationRatioPlanned = 1,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda1 = c(0.0533, 0.0309, 1.5 * 0.0533, 1.5 * 0.0309),
lambda2 = c(0.0533, 0.0533, 1.5 * 0.0533, 1.5 * 0.0533),
gamma1 = -log(1 - 0.05) / 12,
gamma2 = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = 18,
fixedFollowup = FALSE
)
km_one_sided <- kmpower1s(
kMax = 2,
informationRates = c(0.8, 1),
alpha = 0.025,
typeAlphaSpending = "sfOF",
milestone = 18,
survH0 = 0.30,
accrualTime = seq(0, 8),
accrualIntensity = 26 / 9 * seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
stratumFraction = c(0.2, 0.8),
lambda = c(0.0533, 0.0309, 1.5 * 0.0533, 1.5 * 0.0309),
gamma = -log(1 - 0.05) / 12,
accrualDuration = 22,
followupTime = 18,
fixedFollowup = FALSE
)
testthat::expect_s3_class(km_eq, "kmpowerequiv")
testthat::expect_equal(round(km_eq$overallResults$overallReject, 4), 0.8609)
testthat::expect_true(km_eq$overallResults$overallReject > km_eq_off$overallResults$overallReject)
testthat::expect_true(km_one_sided$overallResults$overallReject > km_two_sided$overallResults$overallReject)
})
testthat::test_that("kmest: estimate and standard error", {
library(survival)
df1 <- kmest(aml, stratum="x", time="time", event="status",
conftype="none")
df2 <- summary(survfit(Surv(time, status) ~ x, data=aml,
conf.type="none"))
testthat::expect_equal(df1$surv[df1$time > 0], df2$surv)
testthat::expect_equal(df1$sesurv[df1$time > 0], df2$std.err)
})
testthat::test_that("kmest: confidence interval", {
library(survival)
for (conftype in c("plain", "log", "log-log", "arcsin")) {
df1 <- kmest(aml, stratum="x", time="time", event="status",
conftype=conftype)
df2 <- summary(survfit(Surv(time, status) ~ x, data=aml,
conf.type=conftype))
testthat::expect_equal(df1$lower[df1$time > 0], df2$lower)
testthat::expect_equal(df1$upper[df1$time > 0], df2$upper)
}
})
testthat::test_that("rmdiff: unstratified test of rmst difference", {
library(survival)
df1 <- rmdiff(veteran, treat="trt", time="time",
event="status", milestone=90)
# the following values are obtained from SAS PROC LIFETEST:
# proc lifetest data=veteran rmst(tau=90);
# time time*status(0);
# strata trt;
# run;
rmst1 = 62.75618
rmst2 = 56.45116
stderr1 = 4.0321
stderr2 = 3.9790
rmstdiffchisq = 1.2388
pvalue = 0.2657
testthat::expect_equal(c(round(df1$rmst1, 5),
round(df1$rmst2, 5),
round(sqrt(df1$vrmst1), 4),
round(sqrt(df1$vrmst2), 4),
round(df1$rmstDiffZ^2, 4),
round(df1$rmstDiffPValue, 4)),
c(rmst1, rmst2, stderr1, stderr2,
rmstdiffchisq, pvalue))
})
testthat::test_that("rmdiff: stratified test of rmst difference", {
library(survival)
df1 <- rmdiff(veteran, stratum="celltype", treat="trt",
time="time", event="status", milestone=90)
# Of note, the stratified results are different from SAS PROC LIFETEST:
# proc lifetest data=veteran rmst(tau=90);
# time time*status(0);
# strata celltype / group = trt;
# run;
# This is because SAS adds up the rmst diffs and variances across strata,
# while we use the number of subjects as the weight for each stratum.
# Our approach yields a more meaningful rmst diff across strata.
# To reproduce our results, we combine the stratum-specific information
# from SAS PROC LIFETEST using the sample size weights across strata
n = c(15, 20, 30, 18, 9, 18, 15, 12)
rmst = c(68.55152, 65.95, 50.86667, 45.16667, 56.44444, 51.91667,
84.8, 64.25)
stderr = c(8.3317, 7.5975, 5.7978, 7.8103, 12.7986, 6.9569,
5.0237, 8.0847)
a = c(1, 3, 5, 7)
b = c(2, 4, 6, 8)
ns = n[a] + n[b]
rmstDiffs = rmst[a] - rmst[b]
vrmstDiffs = stderr[a]^2 + stderr[b]^2
w = ns/sum(ns)
rmstDiff = sum(w*rmstDiffs)
sermstDiff = sqrt(sum(w*w*vrmstDiffs))
rmstDiffZ = rmstDiff/sermstDiff
testthat::expect_equal(c(round(df1$rmstDiff, 4),
round(df1$sermstDiff, 3),
round(df1$rmstDiffZ, 3)),
c(round(rmstDiff, 4),
round(sermstDiff, 3),
round(rmstDiffZ, 3)))
})
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