find.lambda_0.given.Delta: Function to solve for the baseline recurrent event rate given...

In gsMeanFreq: Group Sequential Clinical Trial Designs for Composite Endpoints

Function to solve for the baseline recurrent event rate given a mean frequency difference for one-sample simulations.

Description

Computes the value of the baseline recurrent event rate parameter \lambda_0 hat yields a specified difference in mean frequency function at a given event time, relative to a null value.

Usage

find.lambda_0.given.Delta(
  lambda_star,
  null.lambda_0,
  gamma_shape,
  gamma_scale,
  Delta,
  t
)

Arguments

lambda_star	Rate parameter of an exponential distribution in generating the terminal event.
null.lambda_0	Baseline recurrent event rate under the null hypothesis.
gamma_shape	Shape parameter of the Gamma frailty distribution.
gamma_scale	Scale parameter of the Gamma frailty distribution.
Delta	Target difference in the mean frequency function at time t between the null and alternative.
t	Event time (time since enrollment) at which the mean frequency difference is defined.

Value

A numeric value giving the baseline recurrent event rate \lambda_0 that satisfies the specified mean frequency difference at time t.

Examples

# Toy Example: n = 800, two interim analysi planned
# at 50% and 75% if maximum information, along with
# a final analysis at 100% maximum information.
# Assuming the maximum information is 100,
# overall Type I error at 0.05 and power at 0.8.
find.Delta.given.power(K = 3, timing = c(0.5, 0.75, 1),
alpha = 0.05, power = 0.8, Imax = 100)
# Use the computed value from the
# "find.Delta.given.power()" function
find.lambda_0.given.Delta(lambda_star = 0.1, null.lambda_0 = 1.15,
gamma_shape = 2, gamma_scale = 0.5, Delta = 0.2834591, t = 2)

gsMeanFreq documentation built on Feb. 17, 2026, 1:07 a.m.