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_ahr(
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,
interval = c(0.01, 1000)
)Arguments
- enroll_rate
Enrollment rates.
- 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.
- interval
An interval that is presumed to include the time at which expected event count is equal to targeted event.
Value
A data frame with columns Analysis, Time, AHR, Events, 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.
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.
Examples
library(gsDesign)
library(gsDesign2)
# Example 1 ----
# \donttest{
# Only put in targeted events
gs_info_ahr(event = c(30, 40, 50))
#> analysis time event ahr theta info info0
#> 1 1 14.90817 30.00008 0.7865726 0.2400702 7.373433 7.50002
#> 2 2 19.16437 40.00000 0.7442008 0.2954444 9.789940 10.00000
#> 3 3 24.54264 50.00000 0.7128241 0.3385206 12.227632 12.50000
# }
# Example 2 ----
# Only put in targeted analysis times
gs_info_ahr(analysis_time = c(18, 27, 36))
#> analysis time event ahr theta info info0
#> 1 1 18 37.59032 0.7545471 0.2816376 9.208013 9.397579
#> 2 2 27 54.01154 0.7037599 0.3513180 13.216112 13.502885
#> 3 3 36 66.23948 0.6833395 0.3807634 16.267921 16.559870
# Example 3 ----
# \donttest{
# Some analysis times after time at which targeted event accrue
# Check that both Time >= input analysis_time and event >= input event
gs_info_ahr(event = c(30, 40, 50), analysis_time = c(16, 19, 26))
#> analysis time event ahr theta info info0
#> 1 1 16.00000 33.06876 0.7759931 0.2536117 8.118487 8.267189
#> 2 2 19.16437 40.00000 0.7442008 0.2954444 9.789940 10.000001
#> 3 3 26.00000 52.41802 0.7071808 0.3464689 12.822714 13.104505
gs_info_ahr(event = c(30, 40, 50), analysis_time = c(14, 20, 24))
#> analysis time event ahr theta info info0
#> 1 1 14.90817 30.00008 0.7865726 0.2400702 7.373433 7.50002
#> 2 2 20.00000 41.67282 0.7377944 0.3040901 10.195150 10.41821
#> 3 3 24.54264 50.00000 0.7128241 0.3385206 12.227632 12.50000
# }