lrsamplesizeequiv | R Documentation |
Obtains the sample size for equivalence in hazard ratio.
lrsamplesizeequiv(
beta = 0.2,
kMax = 1L,
informationRates = NA_real_,
criticalValues = NA_real_,
alpha = 0.05,
typeAlphaSpending = "sfOF",
parameterAlphaSpending = NA_real_,
userAlphaSpending = NA_real_,
hazardRatioLower = NA_real_,
hazardRatioUpper = 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,
typeOfComputation = "direct",
interval = as.numeric(c(0.001, 240)),
spendingTime = NA_real_,
rounding = 1L
)
beta |
The type II error. |
kMax |
The maximum number of stages. |
informationRates |
The information rates.
Defaults to |
criticalValues |
Upper boundaries on the z-test statistic scale for stopping for efficacy. |
alpha |
The significance level for each of the two one-sided tests. Defaults to 0.05. |
typeAlphaSpending |
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". |
parameterAlphaSpending |
The parameter value for the alpha spending. Corresponds to Delta for "WT", rho for "sfKD", and gamma for "sfHSD". |
userAlphaSpending |
The user defined alpha spending. Cumulative alpha spent up to each stage. |
hazardRatioLower |
The lower equivalence limit of hazard ratio. |
hazardRatioUpper |
The upper equivalence limit of hazard ratio. |
allocationRatioPlanned |
Allocation ratio for the active treatment versus control. Defaults to 1 for equal randomization. |
accrualTime |
A vector that specifies the starting time of
piecewise Poisson enrollment time intervals. Must start with 0, e.g.,
|
accrualIntensity |
A vector of accrual intensities. One for each accrual time interval. |
piecewiseSurvivalTime |
A vector that specifies the starting time of
piecewise exponential survival time intervals. Must start with 0, e.g.,
|
stratumFraction |
A vector of stratum fractions that sum to 1. Defaults to 1 for no stratification. |
lambda1 |
A vector of hazard rates for the event in each analysis time interval by stratum for the active treatment group. |
lambda2 |
A vector of hazard rates for the event in each analysis time interval by stratum for the control group. |
gamma1 |
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. |
gamma2 |
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. |
accrualDuration |
Duration of the enrollment period. |
followupTime |
Follow-up time for the last enrolled subject. |
fixedFollowup |
Whether a fixed follow-up design is used. Defaults to 0 for variable follow-up. |
typeOfComputation |
The type of computation, either "direct" for the direct approximation method, or "schoenfeld" for the Schoenfeld method. Defaults to "direct". Can use "Schoenfeld" under proportional hazards and conventional log-rank test. |
interval |
The interval to search for the solution of
accrualDuration, followupDuration, or the proportionality constant
of accrualIntensity. Defaults to |
spendingTime |
A vector of length |
rounding |
Whether to round up sample size. Defaults to 1 for sample size rounding. |
An S3 class lrpowerequiv
object
Kaifeng Lu, kaifenglu@gmail.com
lrpowerequiv
lrsamplesizeequiv(kMax = 2, informationRates = c(0.5, 1),
alpha = 0.05, typeAlphaSpending = "sfOF",
hazardRatioLower = 0.71, hazardRatioUpper = 1.4,
allocationRatioPlanned = 1, accrualTime = seq(0, 8),
accrualIntensity = 26/9*seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
lambda1 = c(0.0533, 0.0533),
lambda2 = c(0.0533, 0.0533),
gamma1 = -log(1-0.05)/12,
gamma2 = -log(1-0.05)/12, accrualDuration = NA,
followupTime = 18, fixedFollowup = FALSE)
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