Obtains the number of subjects having an event in each treatment group by stratum, the mean and variance of weighted log-rank score statistic for a hypothesized hazard ratio at a given calendar time.
lrstat1(
time = NA_real_,
hazardRatioH0 = 1,
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,
rho1 = 0,
rho2 = 0,
predictEventOnly = 0L
)A data frame of the following variables if
predictEventOnly = 1:
stratum: The stratum number.
time: The analysis time since trial start.
subjects: The number of enrolled subjects.
nevents: The total number of events.
nevents1: The number of events in the active treatment group.
nevents2: The number of events in the control group.
ndropouts: The total number of dropouts.
ndropouts1: The number of dropouts in the active treatment
group.
ndropouts2: The number of dropouts in the control group.
nfmax: The total number of subjects reaching maximum follow-up.
nfmax1: The number of subjects reaching maximum follow-up in
the active treatment group.
nfmax2: The number of subjects reaching maximum follow-up in
the control group.
If predictEventOnly = 0, the following variables will also
be included:
uscore: The numerator of the weighted log-rank test statistic.
vscore: The variance of the weighted log-rank score statistic.
iscore: The Fisher information of the weighted log-rank score
statistic.
The calendar time at which to calculate the number of events and the mean and variance of log-rank test score statistic.
Hazard ratio under the null hypothesis for the active treatment versus control. Defaults to 1 for superiority test.
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 first parameter of the Fleming-Harrington family of weighted log-rank test. Defaults to 0 for conventional log-rank test.
The second parameter of the Fleming-Harrington family of weighted log-rank test. Defaults to 0 for conventional log-rank test.
Whether to predict the number of events only. Defaults to 0 for obtaining log-rank test score statistic mean and variance.
Kaifeng Lu, kaifenglu@gmail.com
# Piecewise accrual, piecewise exponential survivals, and 5% dropout by
# the end of 1 year.
lrstat1(time = 22, hazardRatioH0 = 1,
allocationRatioPlanned = 1,
accrualTime = seq(0, 8),
accrualIntensity = 26/9*seq(1, 9),
piecewiseSurvivalTime = c(0, 6),
lambda1 = c(0.0533, 0.0309),
lambda2 = c(0.0533, 0.0533),
gamma1 = -log(1-0.05)/12,
gamma2 = -log(1-0.05)/12,
accrualDuration = 22,
followupTime = 18, fixedFollowup = FALSE)
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