Based on piecewise enrollment rate, failure rate, and dropout rates computes approximate information and effect size using an average hazard ratio model.
Usage
gs_info_wlr(
enrollRates = tibble::tibble(Stratum = "All", duration = c(2, 2, 10), rate = c(3, 6,
9)),
failRates = tibble::tibble(Stratum = "All", duration = c(3, 100), failRate =
log(2)/c(9, 18), hr = c(0.9, 0.6), dropoutRate = rep(0.001, 2)),
ratio = 1,
events = NULL,
analysisTimes = NULL,
weight = wlr_weight_fh,
approx = "asymptotic"
)Arguments
- enrollRates
enrollment rates
- failRates
failure and dropout rates
- ratio
Experimental:Control randomization ratio
- events
Targeted minimum events at each analysis
- analysisTimes
Targeted minimum study duration at each analysis
- weight
weight of weighted log rank test
"1"=unweighted,"n"=Gehan-Breslow,"sqrtN"=Tarone-Ware,"FH_p[a]_q[b]"= Fleming-Harrington with p=a and q=b
- approx
approximate estimation method for Z statistics
"event driven"= only work under proportional hazard model with log rank test"asymptotic"
Value
a tibble with columns Analysis, Time, N, Events, AHR, delta, sigma2, theta, info, info0.
info, info0 contains statistical information under H1, H0, respectively.
For analysis k, Time[k] is the maximum of analysisTimes[k] and the expected time
required to accrue the targeted events[k].
AHR is expected average hazard ratio at each analysis.