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.

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.

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.

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)
#> Group sequential design (method=; k=3 analyses; Two-sided asymmetric with non-binding futility)
#> N=9064.0 subjects | D=69.0 events | T=24.2 study duration | accrual=16.0 Accrual duration | minfup=8.2 minimum follow-up | ratio=3 randomization ratio (experimental/control)
#> 
#> Spending functions:
#>   Efficacy bounds derived using a Hwang-Shih-DeCani spending function with gamma = -4.
#>   Futility bounds derived using a Lan-DeMets O'Brien-Fleming approximation spending function (no parameters).
#> 
#> Analysis summary:
#>    Analysis              Value Efficacy Futility
#>   IA 1: 45%                  Z   2.8273   0.0695
#>     N: 8626        p (1-sided)   0.0023   0.4723
#>  Events: 31       ~HR at bound   0.2167   0.6801
#>   Month: 15 P(Cross) if HR=0.7   0.0023   0.5277
#>             P(Cross) if HR=0.3   0.2863   0.0141
#>   IA 2: 70%                  Z   2.5197   1.1139
#>     N: 9064        p (1-sided)   0.0059   0.1327
#>  Events: 48       ~HR at bound   0.3022   0.4829
#>   Month: 19 P(Cross) if HR=0.7   0.0071   0.8716
#>             P(Cross) if HR=0.3   0.6265   0.0486
#>       Final                  Z   2.0035   2.0035
#>     N: 9064        p (1-sided)   0.0226   0.0226
#>  Events: 69       ~HR at bound   0.4010   0.4010
#>   Month: 24 P(Cross) if HR=0.7   0.0230   0.9770
#>             P(Cross) if HR=0.3   0.9027   0.0973
#> 
#> Key inputs (names preserved):
#>                                desc    item value   input
#>                     Accrual rate(s)   gamma 566.5   gamma
#>            Accrual rate duration(s)       R    16       R
#>              Control hazard rate(s) lambdaC 0.001 lambdaC
#>             Control dropout rate(s)     eta     0     eta
#>        Experimental dropout rate(s)    etaE     0    etaE
#>  Event and dropout rate duration(s)       S  NULL       S