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Group sequential design for negative binomial outcomes

Source: R/gsNBCalendar.R
gsNBCalendar.Rd

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
)

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().

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.

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)
)