expected_time: Predict time at which a targeted event count is achieved

In gsDesign2: Group Sequential Design with Non-Constant Effect

Predict time at which a targeted event count is achieved

Description

expected_time() is made to match input format with ahr() and to solve for the time at which the expected accumulated events is equal to an input target. Enrollment and failure rate distributions are specified as follows. The piecewise exponential distribution allows a simple method to specify a distribution and enrollment pattern where the enrollment, failure and dropout rates changes over time.

Usage

expected_time(
  enroll_rate = define_enroll_rate(duration = c(2, 2, 10), rate = c(3, 6, 9) * 5),
  fail_rate = define_fail_rate(stratum = "All", duration = c(3, 100), fail_rate =
    log(2)/c(9, 18), hr = c(0.9, 0.6), dropout_rate = rep(0.001, 2)),
  target_event = 150,
  ratio = 1,
  interval = c(0.01, 100)
)

Arguments

enroll_rate	An enroll_rate data frame with or without stratum created by define_enroll_rate().
fail_rate	A fail_rate data frame with or without stratum created by define_fail_rate().
target_event	The targeted number of events to be achieved.
ratio	Experimental:Control randomization ratio.
interval	An interval that is presumed to include the time at which expected event count is equal to target_event.

Value

A data frame with Time (computed to match events in target_event), AHR (average hazard ratio), Events (target_event input), info (information under given scenarios), and info0 (information under related null hypothesis) for each value of total_duration input.

Specification

Examples

# Example 1 ----
# default

expected_time()


# Example 2 ----
# check that result matches a finding using AHR()
# Start by deriving an expected event count
enroll_rate <- define_enroll_rate(duration = c(2, 2, 10), rate = c(3, 6, 9) * 5)
fail_rate <- define_fail_rate(
  duration = c(3, 100),
  fail_rate = log(2) / c(9, 18),
  hr = c(.9, .6),
  dropout_rate = .001
)
total_duration <- 20
xx <- ahr(enroll_rate, fail_rate, total_duration)
xx

# Next we check that the function confirms the timing of the final analysis.

expected_time(enroll_rate, fail_rate,
  target_event = xx$event, interval = c(.5, 1.5) * xx$time
)


# Example 3 ----
# In this example, we verify `expected_time()` by `ahr()`.

x <- ahr(
  enroll_rate = enroll_rate, fail_rate = fail_rate,
  ratio = 1, total_duration = 20
)

cat("The number of events by 20 months is ", x$event, ".\n")

y <- expected_time(
  enroll_rate = enroll_rate, fail_rate = fail_rate,
  ratio = 1, target_event = x$event
)

cat("The time to get ", x$event, " is ", y$time, "months.\n")

gsDesign2 documentation built on April 3, 2025, 9:39 p.m.