test_that("within-trial monotonicity conditions coincide with published results (see Tab. 2, Baumann et al. 2022)", { # Baumann, L., Krisam, J., & Kieser, M. (2022). Monotonicity conditions for # avoiding counterintuitive decisions in basket trials. Biometrical Journal, # 64(5), 934-947. design3 <- setup_fujikawa_x(k = 3, shape1 = 1, shape2 = 1, p0 = 0.2) design4 <- setup_fujikawa_x(k = 4, shape1 = 1, shape2 = 1, p0 = 0.2) expect_equal(check_mon_within(design = design3, n = 24, lambda = 0.99, weight_fun = baskexact::weights_fujikawa, weight_params = list(epsilon = 5, tau = 0.4, logbase = 2), details = FALSE), TRUE) expect_equal(check_mon_within(design = design4, n = 24, lambda = 0.99, weight_fun = weights_jsd, weight_params = list(epsilon = 7, tau = 0.3, logbase = 2), details = FALSE), FALSE) }) test_that("between-trial monotonicity conditions coincide with published results (see Tab. 2, Baumann et al. 2022)", { # Baumann, L., Krisam, J., & Kieser, M. (2022). Monotonicity conditions for # avoiding counterintuitive decisions in basket trials. Biometrical Journal, # 64(5), 934-947. design3 <- setup_fujikawa_x(k = 3, shape1 = 1, shape2 = 1, p0 = 0.2) design4 <- setup_fujikawa_x(k = 4, shape1 = 1, shape2 = 1, p0 = 0.2) expect_equal(check_mon_between(design = design3, n = 24, lambda = 0.99, weight_fun = baskexact::weights_fujikawa, weight_params = list(epsilon = 3, tau = 0.1, logbase = 2), details = FALSE), FALSE) expect_equal(check_mon_between(design = design4, n = 24, lambda = 0.99, weight_fun = weights_jsd, weight_params = list(epsilon = 7, tau = 0.4, logbase = 2), details = FALSE), TRUE) })