gsDesignNB (version 0.2.6)

gsNBCalendar: Group sequential design for negative binomial outcomes

Description

Creates a group sequential design for negative binomial outcomes based on sample size calculations from sample_size_nbinom().

Usage

gsNBCalendar(
  x,
  k = 3,
  test.type = 4,
  alpha = 0.025,
  beta = 0.1,
  astar = 0,
  delta = 0,
  sfu = gsDesign::sfHSD,
  sfupar = -4,
  sfl = gsDesign::sfHSD,
  sflpar = -2,
  tol = 1e-06,
  r = 18,
  usTime = NULL,
  lsTime = NULL,
  analysis_times = NULL
)

Value

An object of class gsNB which inherits from gsDesign

and sample_size_nbinom_result. While the final sample size would be planned total enrollment, interim analysis sample sizes are the expected number enrolled at the times specified in analysis_times. Output value contains all elements from gsDesign::gsDesign() plus:

nb_design

The original sample_size_nbinom_result object

n1

A vector with sample size per analysis for group 1

n2

A vector with sample size per analysis for group 2

n_total

A vector with total sample size per analysis

events

A vector with expected total events per analysis

events1

A vector with expected events per analysis for group 1

events2

A vector with expected events per analysis for group 2

exposure

A vector with expected average calendar exposure per analysis

exposure_at_risk1

A vector with expected at-risk exposure per analysis for group 1

exposure_at_risk2

A vector with expected at-risk exposure per analysis for group 2

variance

A vector with variance of log rate ratio per analysis

T

Calendar time at each analysis (if analysis_times provided)

Note that n.I in the returned object represents the statistical information at each analysis.

Arguments

x

An object of class sample_size_nbinom_result from sample_size_nbinom().

k

Number of analyses (interim + final). Default is 3.

test.type

Test type as in gsDesign::gsDesign():

1

One-sided

2

Two-sided symmetric

3

Two-sided, asymmetric, binding futility bound, beta-spending

4

Two-sided, asymmetric, non-binding futility bound, beta-spending

5

Two-sided, asymmetric, binding futility bound, lower spending

6

Two-sided, asymmetric, non-binding futility bound, lower spending

Default is 4.

alpha

Type I error (one-sided). Default is 0.025.

beta

Type II error (1 - power). Default is 0.1.

astar

Allocated Type I error for lower bound for test.type = 5 or 6. Default is 0.

delta

Standardized effect size. Default is 0 (computed from design).

sfu

Spending function for upper bound. Default is gsDesign::sfHSD.

sfupar

Parameter for upper spending function. Default is -4.

sfl

Spending function for lower bound. Default is gsDesign::sfHSD.

sflpar

Parameter for lower spending function. Default is -2.

tol

Tolerance for convergence. Default is 1e-06.

r

Integer controlling grid size for numerical integration. Default is 18.

usTime

Spending time for upper bound (optional).

lsTime

Spending time for lower bound (optional).

analysis_times

Vector of calendar times for each analysis. Must have length k. These times are stored in the T element and displayed by gsDesign::gsBoundSummary().

References

Jennison, C. and Turnbull, B.W. (2000), Group Sequential Methods with Applications to Clinical Trials. Boca Raton: Chapman and Hall.

Examples

# First create a sample size calculation
nb_ss <- sample_size_nbinom(
  lambda1 = 0.5, lambda2 = 0.3, dispersion = 0.1, power = 0.9,
  accrual_rate = 10, accrual_duration = 20, trial_duration = 24
)

# Then create a group sequential design with analysis times
gs_design <- gsNBCalendar(nb_ss,
  k = 3, test.type = 4,
  analysis_times = c(10, 18, 24)
)