empSignificant {simIDM}

Empirical Significance for a List of Simulated Trials

Description

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.

Usage

empSignificant(simTrials, criticalPFS, criticalOS, eventNumPFS, eventNumOS)

Arguments

simTrials

(list)
simulated trial data sets, see getClinicalTrials().

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.

Value

This returns values of four measures of empirical significance.

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)
)
empSignificant(
  simTrials = simTrials, criticalPFS = 2.4, criticalOS = 2.2,
  eventNumPFS = 300, eventNumOS = 500
)