| kmpowerequiv | R Documentation |
Obtains the power for equivalence in milestone survival probability difference.
kmpowerequiv(
kMax = 1L,
informationRates = NA_real_,
criticalValues = NA_real_,
alpha = 0.05,
typeAlphaSpending = "sfOF",
parameterAlphaSpending = NA_real_,
userAlphaSpending = NA_real_,
milestone = NA_real_,
survDiffLower = NA_real_,
survDiffUpper = 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,
spendingTime = NA_real_,
studyDuration = NA_real_
)
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. |
milestone |
The milestone time at which to calculate the survival probability. |
survDiffLower |
The lower equivalence limit of milestone survival probability difference. |
survDiffUpper |
The upper equivalence limit of milestone survival probability difference. |
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. |
spendingTime |
A vector of length |
studyDuration |
Study duration for fixed follow-up design.
Defaults to missing, which is to be replaced with the sum of
|
An S3 class kmpowerequiv object with 4 components:
overallResults: A data frame containing the following variables:
overallReject: The overall rejection probability.
alpha: The overall significance level.
numberOfEvents: The total number of events.
numberOfSubjects: The total number of subjects.
studyDuration: The total study duration.
information: The maximum information.
expectedNumberOfEvents: The expected number of events.
expectedNumberOfSubjects: The expected number of subjects.
expectedStudyDuration: The expected study duration.
expectedInformation: The expected information.
kMax: The number of stages.
milestone: The milestone time relative to randomization.
survDiffLower: The lower equivalence limit of milestone
survival probability difference.
survDiffUpper: The upper equivalence limit of milestone
survival probability difference.
surv1: The milestone survival probability for the
treatment group.
surv2: The milestone survival probability for the
control group.
survDiff: The milestone survival probability difference.
accrualDuration: The accrual duration.
followupTime: The follow-up duration.
fixedFollowup: Whether a fixed follow-up design is used.
byStageResults: A data frame containing the following variables:
informationRates: The information rates.
efficacyBounds: The efficacy boundaries on the Z-scale for
each of the two one-sided tests.
rejectPerStage: The probability for efficacy stopping.
cumulativeRejection: The cumulative probability for efficacy
stopping.
cumulativeAlphaSpent: The cumulative alpha for each of
the two one-sided tests.
cumulativeAttainedAlphaH10: The cumulative alpha attained
under H10.
cumulativeAttainedAlphaH20: The cumulative alpha attained
under H20.
numberOfEvents: The number of events.
numberOfDropouts: The number of dropouts.
numberOfSubjects: The number of subjects.
numberOfMilestone: The number of subjects reaching
milestone.
analysisTime: The average time since trial start.
efficacySurvDiffLower: The efficacy boundaries on the
milestone survival probability difference scale for the one-sided
null hypothesis at the lower equivalence limit.
efficacySurvDiffUpper: The efficacy boundaries on the
milestone survival probability difference scale for the one-sided
null hypothesis at the upper equivalence limit.
efficacyP: The efficacy bounds on the p-value scale for
each of the two one-sided tests.
information: The cumulative information.
settings: A list containing the following input parameters:
typeAlphaSpending, parameterAlphaSpending,
userAlphaSpending, allocationRatioPlanned,
accrualTime, accuralIntensity,
piecewiseSurvivalTime, stratumFraction,
lambda1, lambda2, gamma1, gamma2,
and spendingTime.
byTreatmentCounts: A list containing the following counts by
treatment group:
numberOfEvents1: The number of events by stage for
the treatment group.
numberOfDropouts1: The number of dropouts by stage for
the treatment group.
numberOfSubjects1: The number of subjects by stage for
the treatment group.
numberOfMilestone1: The number of subjects reaching
milestone by stage for the active treatment group.
numberOfEvents2: The number of events by stage for
the control group.
numberOfDropouts2: The number of dropouts by stage for
the control group.
numberOfSubjects2: The number of subjects by stage for
the control group.
numberOfMilestone2: The number of subjects reaching
milestone by stage for the control group.
expectedNumberOfEvents1: The expected number of events for
the treatment group.
expectedNumberOfDropouts1: The expected number of dropouts
for the active treatment group.
expectedNumberOfSubjects1: The expected number of subjects
for the active treatment group.
expectedNumberOfMilestone1: The expected number of subjects
reaching milestone for the active treatment group.
expectedNumberOfEvents2: The expected number of events for
control group.
expectedNumberOfDropouts2: The expected number of dropouts
for the control group.
expectedNumberOfSubjects2: The expected number of subjects
for the control group.
expectedNumberOfMilestone2: The expected number of subjects
reaching milestone for the control group.
Kaifeng Lu, kaifenglu@gmail.com
kmstat
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)
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