Printing method for optimal two-stage goldstandard designs
Source:R/design_display_functions.R
print.TwoStageGoldStandardDesign.RdPrinting method for optimal two-stage goldstandard designs
Usage
# S3 method for TwoStageGoldStandardDesign
print(x, ...)Value
returns the input x invisibly. This functions is used for its side effects, i.e. printing design characteristics to the screen.
Examples
# Should take about 15 seconds.
# \donttest{
optimize_design_twostage(
cT2 = 1,
cP2 = quote(cP1),
cC2 = quote(cC1),
bTP1f = -Inf,
bTC1f = -Inf,
beta = 0.2,
alternative_TP = 0.4,
alternative_TC = 0,
Delta = 0.2,
binding_futility = TRUE,
lambda = .9,
kappa = 1,
nloptr_opts = list(algorithm = "NLOPT_LN_SBPLX", ftol_rel = 1e-01)
)
#>
iteration: 1/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.250000, 1.000000, 2.105099, 2.270933) f(x) = 915.8373
iteration: 2/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.250000, 1.000000, 2.105099, 2.270933) f(x) = 915.8373
iteration: 3/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.250000, 1.000000, 2.105099, 2.270933) f(x) = 915.8373
iteration: 4/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.428125, 1.000000, 2.105099, 2.270933) f(x) = 917.2013
iteration: 5/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.250000, 1.712500, 2.105099, 2.270933) f(x) = 1022.588
iteration: 6/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.428125, 0.287500, 2.105099, 2.270933) f(x) = 1412.46
iteration: 7/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.2945313, 1.3562500, 2.1050990, 2.2709330) f(x) = 935.5908
iteration: 8/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3835937, 0.6437500, 2.1050990, 2.2709330) f(x) = 963.2407
iteration: 9/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3167969, 1.1781250, 2.1050990, 2.2709330) f(x) = 911.3385
iteration: 10/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.1386719, 1.1781250, 2.1050990, 2.2709330) f(x) = 1108.736
iteration: 11/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.1050990, 2.2709330) f(x) = 904.044
iteration: 12/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.2139503, 2.2709330) f(x) = 908.8018
iteration: 13/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.1050990, 2.5041598) f(x) = 907.0289
iteration: 14/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 1.9962477, 2.5041598) f(x) = 918.6257
iteration: 15/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.1595246, 2.3292397) f(x) = 903.9803
iteration: 16/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.1595246, 2.0960129) f(x) = 935.7317
iteration: 17/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.1187054, 2.4021231) f(x) = 903.2534
iteration: 18/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.1731310, 2.4604297) f(x) = 906.1748
iteration: 19/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.1221070, 2.3183072) f(x) = 902.9637
iteration: 20/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.0812878, 2.3911905) f(x) = 903.0437
iteration: 21/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.0846894, 2.3073747) f(x) = 902.7042
iteration: 22/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.0676814, 2.2600005) f(x) = 904.3596
iteration: 23/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.1255086, 2.2344913) f(x) = 906.9245
iteration: 24/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.0923430, 2.3520157) f(x) = 902.4161
iteration: 25/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.0549254, 2.3410832) f(x) = 903.1108
iteration: 26/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.1053116, 2.3240012) f(x) = 902.5799
iteration: 27/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.1129652, 2.3686423) f(x) = 902.6578
iteration: 28/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.1058963, 2.3533254) f(x) = 902.4788
iteration: 29/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.0929276, 2.3813399) f(x) = 902.7331
iteration: 30/100 cP1 <- x[1L] cC1 <- x[2L] bTP1e <- x[3L] bTC1e <- x[4L] x = c(0.3557617, 1.0445312, 2.1022156, 2.3383359) f(x) = 902.4439
#> Optimization finished. Calculating final design with greater accuracy...
#> Sample sizes (stage 1): T: 209, P: 75, C: 219
#> Sample sizes (stage 2): T: 209, P: 75, C: 219
#> Efficacy boundaries (stage 1): Z_TP_e: 2.09234, Z_TC_e: 2.35202
#> Futility boundaries (stage 1): Z_TP_f: -Inf, Z_TC_f: -Inf
#> Efficacy boundaries (stage 2): Z_TP_e: 2.29395, Z_TC_e: 2.06436
#> Inverse normal combination test weights (TP): w1: 0.70711, w2: 0.70711
#> Inverse normal combination test weights (TC): w1: 0.70711, w2: 0.70711
#> Maximum overall sample size: 1006
#> Expected sample size (H1): 795.7
#> Expected sample size (H0): 1004.2
#> Expected placebo group sample size (H1): 89.4
#> Expected placebo group sample size (H0): 148.6
#> Objective function value: 905.7
#> (local) type I error for TP testing: 2.50%
#> (local) type I error for TC testing: 2.50%
#> Probability of futility stop (H1): 0.00%
#> Probability of futility stop (H0): 0.00%
#> Minimum conditional power: 0.00%
#> Power: 80.03%
#> Futility boundaries: binding
#> Futility testing method: always both futility tests
# }