gs_info_wlr {gsDesign2} | R Documentation |
Information and effect size for weighted log-rank test
Description
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(
enroll_rate = define_enroll_rate(duration = c(2, 2, 10), rate = c(3, 6, 9)),
fail_rate = define_fail_rate(duration = c(3, 100), fail_rate = log(2)/c(9, 18), hr =
c(0.9, 0.6), dropout_rate = 0.001),
ratio = 1,
event = NULL,
analysis_time = NULL,
weight = wlr_weight_fh,
approx = "asymptotic",
interval = c(0.01, 1000)
)
Arguments
enroll_rate |
An |
fail_rate |
Failure and dropout rates. |
ratio |
Experimental:Control randomization ratio. |
event |
Targeted minimum events at each analysis. |
analysis_time |
Targeted minimum study duration at each analysis. |
weight |
Weight of weighted log rank test:
|
approx |
Approximate estimation method for Z statistics.
|
interval |
An interval that is presumed to include the time at which expected event count is equal to targeted event. |
Details
The ahr()
function computes statistical information at targeted event times.
The expected_time()
function is used to get events and average HR at
targeted analysis_time
.
Value
A tibble with columns Analysis, Time, N, Events, AHR, delta, sigma2,
theta, info, info0.
info
and info0
contain statistical information under H1, H0, respectively.
For analysis k
, Time[k]
is the maximum of analysis_time[k]
and the
expected time required to accrue the targeted event[k]
.
AHR
is the expected average hazard ratio at each analysis.
Examples
library(gsDesign2)
# Set enrollment rates
enroll_rate <- define_enroll_rate(duration = 12, rate = 500 / 12)
# Set failure rates
fail_rate <- define_fail_rate(
duration = c(4, 100),
fail_rate = log(2) / 15, # median survival 15 month
hr = c(1, .6),
dropout_rate = 0.001
)
# Set the targeted number of events and analysis time
event <- c(30, 40, 50)
analysis_time <- c(10, 24, 30)
gs_info_wlr(
enroll_rate = enroll_rate, fail_rate = fail_rate,
event = event, analysis_time = analysis_time
)