Build a fahb design object

Given a fahb_problem object, find efficient progression decision rules. These can include rules of the standard "progression criteria form", or rules based on a Bayesian analysis of the pilot trial data, or both.

Usage

fahb_design(problem, quietly = TRUE)

Arguments

problem

an object of class fahb_problem.

quietly

if this argument is set to FASLE then information about which steps have been completed will be printed to the console. Defaults to TRUE.

Value

an object of class fahb_design.

Examples

problem <- forecast(fahb_problem(), n_sims = 500)
fahb_design(problem)
#> Standard progression criteria
#> 
#>     FPR        FNR        n_p        m_p        r_p
#> 1   0.0 0.75136612 16.8772428  1.1274486  3.8351325
#> 11  0.1 0.46174863  4.4350729  3.2596766  7.4832026
#> 21  0.2 0.31147541  8.1299921  1.1851716  4.4659384
#> 41  0.4 0.12841530  3.8520453 -0.7482546  4.4056872
#> 51  0.5 0.09836066  0.7679427  2.1779381  3.8540530
#> 71  0.7 0.06284153  1.4183957  1.2379523  3.1665629
#> 81  0.8 0.03825137  1.4184781  1.1338973  2.6796628
#> 91  0.9 0.01639344  1.2209585  1.1616479  1.1432414
#> 101 1.0 0.00000000  0.1215247 -0.8231258 -0.9920766
#> 
#> Bayesian approximation
#> 
#>     FPR        FNR      T_p
#> 1   0.0 1.00000000 1.610074
#> 11  0.1 0.51092896 3.035218
#> 21  0.2 0.34699454 3.256845
#> 41  0.4 0.15027322 3.579466
#> 51  0.5 0.10655738 3.728152
#> 71  0.7 0.06284153 3.882449
#> 81  0.8 0.05191257 3.980638
#> 91  0.9 0.01912568 4.132130
#> 101 1.0 0.00273224 4.353757
#> 
#> FPR - False Positive Rate
#> FNR - False Negative Rate
#> 
#> n_p, m_p, r_p - Probabilistic thresholds for standard
#>                 progression criteria on the number recruited,
#>                 number of sites opened, and the recruitment rate
#>                 (participants per site per year) respectively
#> 
#> T_p - Bayesian decision rule threshold for the posterior predictive
#>       expected time until full recruitment