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

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

Usage

toInteger(x, ratio = 0, roundUpFinal = TRUE)

Arguments

x

An object of class gsDesign or gsSurv.

ratio

A non-negative integer, usually corresponding to experimental:control sample size ratio. Rounding is done to a multiple of ratio + 1. If input x has class gsSurv (design for time-to-event outcome), and x$ratio is a whole number, ratio is replaced by x$ratio. See details.

roundUpFinal

Final value in returned n.I is rounded up if TRUE; otherwise, just rounded. For gsSurv input, final total sample size is also controlled by this. 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

If ratio = 3, rounding for final sample size is done to a multiple of 3 + 1 = 4. For a gsSurv object input in x, event counts output in n.I are rounded to nearest integer and final total sample size is rounded to a multiple of ratio + 1. For other input values of x (gsDesign class), n.I is interpreted as sample size; final value is rounded to a multiple of ratio + 1, with roundUpFinal controlling rounding of last value.

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 bounds to exact binomial bounds
toInteger(x, ratio = 3)
#> Time to event group sequential design with HR= 0.3 
#> Non-inferiority design with null HR= 0.7 
#> Randomization (Exp/Control):  ratio= 3 
#> Asymmetric two-sided group sequential design with
#> 90 % power and 2.5 % Type I Error.
#> Upper bound spending computations assume
#> trial continues if lower bound is crossed.
#> 
#>                ----Lower bounds----  ----Upper bounds-----
#>   Analysis N   Z   Nominal p Spend+  Z   Nominal p Spend++
#>          1 31 0.07    0.5277 0.0141 2.83    0.0023  0.0023
#>          2 48 1.11    0.8673 0.0345 2.52    0.0059  0.0047
#>          3 69 2.00    0.9774 0.0514 2.00    0.0226  0.0179
#>      Total                   0.1000                 0.0250 
#> + lower bound beta spending (under H1):
#>  Lan-DeMets O'Brien-Fleming approximation spending function (no parameters).
#> ++ alpha spending:
#>  Hwang-Shih-DeCani spending function with gamma = -4.
#> 
#> Boundary crossing probabilities and expected sample size
#> assume any cross stops the trial
#> 
#> Upper boundary (power or Type I Error)
#>           Analysis
#>    Theta      1      2      3  Total E{N}
#>   0.0000 0.0023 0.0047 0.0159 0.0230 41.5
#>   0.4065 0.2863 0.3402 0.2762 0.9027 49.7
#> 
#> Lower boundary (futility or Type II Error)
#>           Analysis
#>    Theta      1      2      3  Total
#>   0.0000 0.5277 0.3439 0.1054 0.9770
#>   0.4065 0.0141 0.0345 0.0487 0.0973
#>              T        n Events HR futility HR efficacy
#> IA 1  15.22419 8624.502     31       0.680       0.217
#> IA 2  19.23447 9064.000     48       0.483       0.302
#> Final 24.19069 9064.000     69       0.401       0.401
#> Accrual rates:
#>      Stratum 1
#> 0-16     566.5
#> Control event rates (H1):
#>       Stratum 1
#> 0-Inf         0
#> Censoring rates:
#>       Stratum 1
#> 0-Inf         0