toInteger: Translate group sequential design to integer events (survival...

In gsDesign: Group Sequential Design

Translate group sequential design to integer events (survival designs) or sample size (other designs)

Description

Translate group sequential design to integer events (survival designs) or sample size (other designs)

Usage

toInteger(x, ratio = x$ratio, roundUpFinal = TRUE)

Arguments

x	An object of class gsDesign or gsSurv.
ratio	Usually corresponds to experimental:control sample size ratio. If an integer is provided, rounding is done to a multiple of ratio + 1. See details. If input is non integer, rounding is done to the nearest integer or nearest larger integer depending on roundUpFinal.
roundUpFinal	Sample size is rounded up to a value of ratio + 1 with the default roundUpFinal = TRUE if ratio is a non-negative integer. If roundUpFinal = FALSE and ratio is a non-negative integer, sample size is rounded to the nearest multiple of ratio + 1. For event counts, roundUpFinal = TRUE rounds final event count up; otherwise, just rounded if roundUpFinal = FALSE. See details.

Details

It is useful to explicitly provide the argument ratio when a gsDesign object is input since gsDesign() does not have a ratio in return. ratio = 0, roundUpFinal = TRUE will just round up the sample size (also event count). Rounding of event count targets is not impacted by ratio. Since x <- gsSurv(ratio = M) returns a value for ratio, toInteger(x) will round to a multiple of M + 1 if M is a non-negative integer; otherwise, just rounding will occur. The most common example would be if there is 1:1 randomization (2:1) and the user wishes an even (multiple of 3) sample size, then toInteger() will operate as expected. To just round without concern for randomization ratio, set ratio = 0. If toInteger(x, ratio = 3), rounding for final sample size is done to a multiple of 3 + 1 = 4; this could represent a 3:1 or 1:3 randomization ratio. For 3:2 randomization, ratio = 4 would ensure rounding sample size to a multiple of 5.

Value

Output is an object of the same class as input x; i.e., gsDesign with integer vector for n.I or gsSurv with integer vector n.I and integer total sample size. See details.

Examples

# The following code derives the group sequential design using the method
# of Lachin and Foulkes

x <- gsSurv(
  k = 3,                 # 3 analyses
  test.type = 4,         # Non-binding futility bound 1 (no futility bound) and 4 are allowable
  alpha = .025,          # 1-sided Type I error
  beta = .1,             # Type II error (1 - power)
  timing = c(0.45, 0.7), # Proportion of final planned events at interims
  sfu = sfHSD,           # Efficacy spending function
  sfupar = -4,           # Parameter for efficacy spending function
  sfl = sfLDOF,          # Futility spending function; not needed for test.type = 1
  sflpar = 0,            # Parameter for futility spending function
  lambdaC = .001,        # Exponential failure rate
  hr = 0.3,              # Assumed proportional hazard ratio (1 - vaccine efficacy = 1 - VE)
  hr0 = 0.7,             # Null hypothesis VE
  eta = 5e-04,           # Exponential dropout rate
  gamma = 10,            # Piecewise exponential enrollment rates
  R = 16,                # Time period durations for enrollment rates in gamma
  T = 24,                # Planned trial duration
  minfup = 8,            # Planned minimum follow-up
  ratio = 3              # Randomization ratio (experimental:control)
)
# Convert sample size to multiple of ratio + 1 = 4, round event counts.
# Default is to round up both event count and sample size for final analysis
toInteger(x)

gsDesign documentation built on April 4, 2025, 2:31 a.m.