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Information and effect size for weighted log-rank test

Source: R/gs_info_wlr.R
gs_info_wlr.Rd

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 enroll_rate data frame with or without stratum created by define_enroll_rate().

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:

  • "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".

interval

An interval that is presumed to include the time at which expected event count is equal to targeted event.

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.

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(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
)
#>   analysis time        n     event       ahr        delta     sigma2     theta
#> 1        1   10 416.6667  77.80361 0.8720599 -0.005325328 0.03890022 0.1368971
#> 2        2   24 500.0001 246.28341 0.7164215 -0.040920239 0.12270432 0.3334865
#> 3        3   30 500.0001 293.69568 0.6955693 -0.052942680 0.14583769 0.3630247
#>       info    info0
#> 1 16.20843 16.22923
#> 2 61.35217 62.08666
#> 3 72.91885 74.25144