gsDesign2 (version 1.1.3)

gs_info_wlr: 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)
)

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.

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.

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
)