This function computes four types of empirical significance — PFS, OS, at-least (significant in at least one of PFS/OS), and joint (significant in both PFS and OS) — using the log-rank test. Empirical significance is calculated as the proportion of significant results in simulated trials, each ending when a set number of PFS/OS events occur. Critical values for PFS and OS test significance must be specified. If trials simulate equal transition hazards across groups (H0), empirical significance estimates type I error; if they simulate differing transition hazards (H1), it estimates power.
Arguments
- simTrials
(
list)
simulated trial data sets, seegetClinicalTrials().- criticalPFS
(positive
number)
critical value of the log-rank test for PFS.- criticalOS
(positive
number)
critical value of the log-rank test for OS.- eventNumPFS
(
integer)
number of PFS events required to trigger PFS analysis.- eventNumOS
(
integer)
number of OS events required to trigger OS analysis.
Examples
transition1 <- exponential_transition(h01 = 0.06, h02 = 0.3, h12 = 0.3)
transition2 <- exponential_transition(h01 = 0.1, h02 = 0.4, h12 = 0.3)
simTrials <- getClinicalTrials(
nRep = 50, nPat = c(800, 800), seed = 1234, datType = "1rowPatient",
transitionByArm = list(transition1, transition2), dropout = list(rate = 0.5, time = 12),
accrual = list(param = "intensity", value = 7)
)
#> Simulating 50 trials:
#>
|
| | 0%
|
|= | 2%
|
|=== | 4%
|
|==== | 6%
|
|====== | 8%
|
|======= | 10%
|
|======== | 12%
|
|========== | 14%
|
|=========== | 16%
|
|============= | 18%
|
|============== | 20%
|
|=============== | 22%
|
|================= | 24%
|
|================== | 26%
|
|==================== | 28%
|
|===================== | 30%
|
|====================== | 32%
|
|======================== | 34%
|
|========================= | 36%
|
|=========================== | 38%
|
|============================ | 40%
|
|============================= | 42%
|
|=============================== | 44%
|
|================================ | 46%
|
|================================== | 48%
|
|=================================== | 50%
|
|==================================== | 52%
|
|====================================== | 54%
|
|======================================= | 56%
|
|========================================= | 58%
|
|========================================== | 60%
|
|=========================================== | 62%
|
|============================================= | 64%
|
|============================================== | 66%
|
|================================================ | 68%
|
|================================================= | 70%
|
|================================================== | 72%
|
|==================================================== | 74%
|
|===================================================== | 76%
|
|======================================================= | 78%
|
|======================================================== | 80%
|
|========================================================= | 82%
|
|=========================================================== | 84%
|
|============================================================ | 86%
|
|============================================================== | 88%
|
|=============================================================== | 90%
|
|================================================================ | 92%
|
|================================================================== | 94%
|
|=================================================================== | 96%
|
|===================================================================== | 98%
|
|======================================================================| 100%
empSignificant(
simTrials = simTrials, criticalPFS = 2.4, criticalOS = 2.2,
eventNumPFS = 300, eventNumOS = 500
)
#> $significantPFS
#> [1] 0.74
#>
#> $significantOS
#> [1] 0.52
#>
#> $significantAtLeastOne
#> [1] 0.78
#>
#> $significantBoth
#> [1] 0.48
#>