opt.design: Zhong's 2-/3- stage Phase II design

In wguo1990/tsdf: Two-/Three-Stage Designs for Phase 1&2 Clinical Trials

Description

calculate optimal 2-/3-stage design given by Bob Zhong

Usage

opt.design(
  alpha1,
  alpha2,
  beta,
  pc,
  pe,
  stage = 2,
  stop.eff = FALSE,
  frac_n1 = NULL,
  frac_n2 = NULL,
  sf.param = NULL,
  show = FALSE,
  nmax = 100,
  n.choice = 1,
  ...
)

Arguments

alpha1	left-side overall type I error.
alpha2	right-side overall type I error.
beta	type II error
pc	a numeric vector of response rate. should be a vector with length 1 or 2.
pe	alternative hypothesis.
stage	2 or 3. default to 2 (2-stage design).
stop.eff	logical flag. default to FALSE. if stop.eff = TRUE, the trial may stop for efficacy at interim.
frac_n1	proportion of n1. for 2-stage design, default to c(0.3, 0.6), i.e. the range of n1 is 0.2*n to 0.5*n. for 3-stage design, default to c(0.2, 0.3), i.e. the range of n1 is 0.2*n to 0.3*n
frac_n2	proportion of n2. Used for 3-stage design. default to c(0.2, 0.4).
sf.param	a single real value specifying the gamma parameter for which Hwang-Shih-DeCani spending is to be computed; allowable range is [-40, 40]. Increasing this parameter implies that more error is spent early stage and less is available in late stage. For two-stage designs, default to NULL(alpha-spending is not used); for three-stage designs, default to 4.
show	logical. If TRUE, current sample size is shown as total sample size increase.
nmax	maximum sample size. default to 100.
n.choice	stop criterion for the search of feasible designs. stop if number of designs is more than n.choice
...	not used argument.

Details

The two-stage design setup is: n1 patients are treated in the first stage. At the end of the first stage, either the trial continues to the second stage or inefficacy is concluded and the trial is stopped (early termination), depending on the number of responses observed at the first stage. If the trial does continue to the second stage, additional n2 patients are treated. Three-stage design is an extension of two-stage design where one stage is added between Stage 1 and 2. The left-side rejection region is response <= r_i for i = 1, 2, or 3 and right-side rejection region is response > s. Alpha-spending method is added to two-/three-stage designs. opt.design supports Hwang-Shih-DeCani spending function. You can change the definition of HSD function to use a different spending function.

Value

An object of class "opt.design" is a list containing:

bdry	rejection regions
error	true type 1/2 errors
n	sample size at each stage
complete	complete list of feasible designs
alpha1	input; left-side type 1 error
alpha2	input; right-side type 1 error
beta	input; type 2 error
pc	input; a vector of response rate.
pe	input; a vector of alternative response rate
sf	input; the alpha-spending function used
stage	input; two- or three- stage design is used

Author(s)

Wenchuan Guo <[e-mail]>, Jianan Hui <[e-mail]>

References

Zhong. (2012) Single-arm Phase IIA clinical trials with go/no-go decisions. Contemporary Clinical Trials, 33, 1272–1279.

Examples

 alpha1 <- 0.15
 alpha2 <- 0.10
 beta <- 0.15
 pc <- 0.25
 pe <- pc + 0.20
 # calculate optimal two-stage design without using alpha-spending
 opt.design(alpha1, alpha2, beta, pc, pe, stage=2)
 ## Not run: 
 # calculate optimal two-stage design with Pocock-like spending function 
 opt.design(alpha1, alpha2, beta, pc, pt, stage = 2, sf.param = 1)
 
 # calculate optimal three-stage design with =O’Brien-Fleming like spending function
 opt.design(alpha1, alpha2, beta, pc, pt, stage = 3, sf.param = -4)
 
## End(Not run)

wguo1990/tsdf documentation built on July 2, 2021, 12:54 a.m.