Obtains the sample size for equivalence in restricted mean survival time difference.
rmsamplesizeequiv(
beta = 0.2,
kMax = 1L,
informationRates = NA_real_,
criticalValues = NA_real_,
alpha = 0.05,
typeAlphaSpending = "sfOF",
parameterAlphaSpending = NA_real_,
userAlphaSpending = NA_real_,
milestone = NA_real_,
rmstDiffLower = NA_real_,
rmstDiffUpper = NA_real_,
allocationRatioPlanned = 1,
accrualTime = 0L,
accrualIntensity = NA_real_,
piecewiseSurvivalTime = 0L,
stratumFraction = 1L,
lambda1 = NA_real_,
lambda2 = NA_real_,
gamma1 = 0L,
gamma2 = 0L,
accrualDuration = NA_real_,
followupTime = NA_real_,
fixedFollowup = 0L,
interval = as.numeric(c(0.001, 240)),
spendingTime = NA_real_,
rounding = 1L
)An S3 class rmpowerequiv object
The type II error.
The maximum number of stages.
The information rates.
Defaults to (1:kMax) / kMax if left unspecified.
Upper boundaries on the z-test statistic scale for stopping for efficacy.
The significance level for each of the two one-sided tests. Defaults to 0.05.
The type of alpha spending. One of the following: "OF" for O'Brien-Fleming boundaries, "P" for Pocock boundaries, "WT" for Wang & Tsiatis boundaries, "sfOF" for O'Brien-Fleming type spending function, "sfP" for Pocock type spending function, "sfKD" for Kim & DeMets spending function, "sfHSD" for Hwang, Shi & DeCani spending function, "user" for user defined spending, and "none" for no early efficacy stopping. Defaults to "sfOF".
The parameter value for the alpha spending. Corresponds to Delta for "WT", rho for "sfKD", and gamma for "sfHSD".
The user defined alpha spending. Cumulative alpha spent up to each stage.
The milestone time at which to calculate the restricted mean survival time.
The lower equivalence limit of restricted mean survival time difference.
The upper equivalence limit of restricted mean survival time difference.
Allocation ratio for the active treatment versus control. Defaults to 1 for equal randomization.
A vector that specifies the starting time of
piecewise Poisson enrollment time intervals. Must start with 0, e.g.,
c(0, 3) breaks the time axis into 2 accrual intervals:
[0, 3) and [3, Inf).
A vector of accrual intensities. One for each accrual time interval.
A vector that specifies the starting time of
piecewise exponential survival time intervals. Must start with 0, e.g.,
c(0, 6) breaks the time axis into 2 event intervals:
[0, 6) and [6, Inf).
Defaults to 0 for exponential distribution.
A vector of stratum fractions that sum to 1. Defaults to 1 for no stratification.
A vector of hazard rates for the event in each analysis time interval by stratum for the active treatment group.
A vector of hazard rates for the event in each analysis time interval by stratum for the control group.
The hazard rate for exponential dropout, a vector of hazard rates for piecewise exponential dropout applicable for all strata, or a vector of hazard rates for dropout in each analysis time interval by stratum for the active treatment group.
The hazard rate for exponential dropout, a vector of hazard rates for piecewise exponential dropout applicable for all strata, or a vector of hazard rates for dropout in each analysis time interval by stratum for the control group.
Duration of the enrollment period.
Follow-up time for the last enrolled subject.
Whether a fixed follow-up design is used. Defaults to 0 for variable follow-up.
The interval to search for the solution of
accrualDuration, followupDuration, or the proportionality constant
of accrualIntensity. Defaults to c(0.001, 240).
A vector of length kMax for the error spending
time at each analysis. Defaults to missing, in which case, it is the
same as informationRates.
Whether to round up sample size. Defaults to 1 for sample size rounding.
Kaifeng Lu, kaifenglu@gmail.com
rmpowerequiv
rmsamplesizeequiv(beta = 0.1, kMax = 2, informationRates = c(0.5, 1),
alpha = 0.05, typeAlphaSpending = "sfOF",
milestone = 18,
rmstDiffLower = -2, rmstDiffUpper = 2,
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)
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