## ----setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 7, fig.height = 5.5, fig.path = "figures/2bin-" ) library(BayesianQDM) ## ----getjointbin-------------------------------------------------------------- # Convert marginal rates + correlation to cell probabilities getjointbin(pi1 = 0.30, pi2 = 0.35, rho = 0.20) getjointbin(pi1 = 0.20, pi2 = 0.20, rho = 0.00) # independence ## ----nine-region-bin, echo = FALSE, results = 'asis'-------------------------- cat('

Nine-region grid for two-endpoint posterior probability
		Endpoint 1
		θ₁ > θ_TV1	θ_TV1 ≥ θ₁ > θ_MAV1	θ_MAV1 ≥ θ₁
Endpoint 2	θ₂ > θ_TV2	R1	R4	R7
	θ_TV2 ≥ θ₂ > θ_MAV2	R2	R5	R8
	θ_MAV2 ≥ θ₂	R3	R6	R9

') ## ----four-region-bin, echo = FALSE, results = 'asis'-------------------------- cat('

Four-region grid for two-endpoint predictive probability
		Endpoint 1
		θ₁ > θ_NULL1	θ₁ ≤ θ_NULL1
Endpoint 2	θ₂ > θ_NULL2	R1	R3
Endpoint 2	θ₂ ≤ θ_NULL2	R2	R4

') ## ----ctrl-post---------------------------------------------------------------- set.seed(42) p_post_ctrl <- pbayespostpred2bin( prob = 'posterior', design = 'controlled', theta_TV1 = 0.20, theta_MAV1 = 0.10, theta_TV2 = 0.20, theta_MAV2 = 0.10, theta_NULL1 = NULL, theta_NULL2 = NULL, x_t_00 = 1L, x_t_01 = 1L, x_t_10 = 2L, x_t_11 = 3L, x_c_00 = 2L, x_c_01 = 1L, x_c_10 = 2L, x_c_11 = 2L, a_t_00 = 0.25, a_t_01 = 0.25, a_t_10 = 0.25, a_t_11 = 0.25, a_c_00 = 0.25, a_c_01 = 0.25, a_c_10 = 0.25, a_c_11 = 0.25, m_t = NULL, m_c = NULL, z00 = NULL, z01 = NULL, z10 = NULL, z11 = NULL, xe_t_00 = NULL, xe_t_01 = NULL, xe_t_10 = NULL, xe_t_11 = NULL, xe_c_00 = NULL, xe_c_01 = NULL, xe_c_10 = NULL, xe_c_11 = NULL, alpha0e_t = NULL, alpha0e_c = NULL, nMC = 1000L ) print(round(p_post_ctrl, 4)) cat(sprintf( "\nGo region (R1): P = %.4f >= gamma_go (0.80)? %s\n", p_post_ctrl["R1"], ifelse(p_post_ctrl["R1"] >= 0.80, "YES -> Go", "NO") )) cat(sprintf( "NoGo region (R9): P = %.4f >= gamma_nogo (0.80)? %s\n", p_post_ctrl["R9"], ifelse(p_post_ctrl["R9"] >= 0.80, "YES -> NoGo", "NO") )) ## ----ctrl-pred---------------------------------------------------------------- set.seed(42) p_pred_ctrl <- pbayespostpred2bin( prob = 'predictive', design = 'controlled', theta_TV1 = NULL, theta_MAV1 = NULL, theta_TV2 = NULL, theta_MAV2 = NULL, theta_NULL1 = 0.15, theta_NULL2 = 0.15, x_t_00 = 1L, x_t_01 = 1L, x_t_10 = 2L, x_t_11 = 3L, x_c_00 = 2L, x_c_01 = 1L, x_c_10 = 2L, x_c_11 = 2L, a_t_00 = 0.25, a_t_01 = 0.25, a_t_10 = 0.25, a_t_11 = 0.25, a_c_00 = 0.25, a_c_01 = 0.25, a_c_10 = 0.25, a_c_11 = 0.25, m_t = 15L, m_c = 15L, z00 = NULL, z01 = NULL, z10 = NULL, z11 = NULL, xe_t_00 = NULL, xe_t_01 = NULL, xe_t_10 = NULL, xe_t_11 = NULL, xe_c_00 = NULL, xe_c_01 = NULL, xe_c_10 = NULL, xe_c_11 = NULL, alpha0e_t = NULL, alpha0e_c = NULL, nMC = 1000L ) print(round(p_pred_ctrl, 4)) cat(sprintf( "\nGo region (R1): P = %.4f >= gamma_go (0.80)? %s\n", p_pred_ctrl["R1"], ifelse(p_pred_ctrl["R1"] >= 0.80, "YES -> Go", "NO") )) ## ----unctrl-post-------------------------------------------------------------- set.seed(1) p_unctrl <- pbayespostpred2bin( prob = 'posterior', design = 'uncontrolled', theta_TV1 = 0.20, theta_MAV1 = 0.10, theta_TV2 = 0.20, theta_MAV2 = 0.10, theta_NULL1 = NULL, theta_NULL2 = NULL, x_t_00 = 1L, x_t_01 = 1L, x_t_10 = 2L, x_t_11 = 3L, x_c_00 = NULL, x_c_01 = NULL, x_c_10 = NULL, x_c_11 = NULL, a_t_00 = 0.25, a_t_01 = 0.25, a_t_10 = 0.25, a_t_11 = 0.25, a_c_00 = 0.25, a_c_01 = 0.25, a_c_10 = 0.25, a_c_11 = 0.25, m_t = NULL, m_c = NULL, z00 = 2L, z01 = 1L, z10 = 2L, z11 = 1L, xe_t_00 = NULL, xe_t_01 = NULL, xe_t_10 = NULL, xe_t_11 = NULL, xe_c_00 = NULL, xe_c_01 = NULL, xe_c_10 = NULL, xe_c_11 = NULL, alpha0e_t = NULL, alpha0e_c = NULL, nMC = 1000L ) print(round(p_unctrl, 4)) ## ----ext-post----------------------------------------------------------------- set.seed(2) p_ext <- pbayespostpred2bin( prob = 'posterior', design = 'external', theta_TV1 = 0.20, theta_MAV1 = 0.10, theta_TV2 = 0.20, theta_MAV2 = 0.10, theta_NULL1 = NULL, theta_NULL2 = NULL, x_t_00 = 1L, x_t_01 = 1L, x_t_10 = 2L, x_t_11 = 3L, x_c_00 = 2L, x_c_01 = 1L, x_c_10 = 2L, x_c_11 = 2L, a_t_00 = 0.25, a_t_01 = 0.25, a_t_10 = 0.25, a_t_11 = 0.25, a_c_00 = 0.25, a_c_01 = 0.25, a_c_10 = 0.25, a_c_11 = 0.25, m_t = NULL, m_c = NULL, z00 = NULL, z01 = NULL, z10 = NULL, z11 = NULL, xe_t_00 = NULL, xe_t_01 = NULL, xe_t_10 = NULL, xe_t_11 = NULL, xe_c_00 = 3L, xe_c_01 = 1L, xe_c_10 = 2L, xe_c_11 = 1L, alpha0e_t = NULL, alpha0e_c = 0.5, nMC = 1000L ) print(round(p_ext, 4)) ## ----ext-borrowing------------------------------------------------------------ ae_seq <- c(0.01, seq(0.1, 1.0, by = 0.1)) p_ae <- sapply(ae_seq, function(ae) { set.seed(99) res <- pbayespostpred2bin( prob = 'posterior', design = 'external', theta_TV1 = 0.20, theta_MAV1 = 0.10, theta_TV2 = 0.20, theta_MAV2 = 0.10, theta_NULL1 = NULL, theta_NULL2 = NULL, x_t_00 = 1L, x_t_01 = 1L, x_t_10 = 2L, x_t_11 = 3L, x_c_00 = 2L, x_c_01 = 1L, x_c_10 = 2L, x_c_11 = 2L, a_t_00 = 0.25, a_t_01 = 0.25, a_t_10 = 0.25, a_t_11 = 0.25, a_c_00 = 0.25, a_c_01 = 0.25, a_c_10 = 0.25, a_c_11 = 0.25, m_t = NULL, m_c = NULL, z00 = NULL, z01 = NULL, z10 = NULL, z11 = NULL, xe_t_00 = NULL, xe_t_01 = NULL, xe_t_10 = NULL, xe_t_11 = NULL, xe_c_00 = 3L, xe_c_01 = 1L, xe_c_10 = 2L, xe_c_11 = 1L, alpha0e_t = NULL, alpha0e_c = ae, nMC = 500L ) res["R1"] }) data.frame(alpha0e_c = ae_seq, P_R1 = round(p_ae, 4)) ## ----oc-controlled, fig.width = 8, fig.height = 6----------------------------- pi_t_seq <- seq(0.20, 0.90, by = 0.10) n_scen <- length(pi_t_seq) oc_ctrl <- pbayesdecisionprob2bin( prob = 'posterior', design = 'controlled', GoRegions = 1L, NoGoRegions = 9L, gamma_go = 0.80, gamma_nogo = 0.80, pi_t1 = rep(pi_t_seq, each = n_scen), pi_t2 = rep(pi_t_seq, times = n_scen), rho_t = rep(0.0, n_scen * n_scen), pi_c1 = rep(0.20, n_scen * n_scen), pi_c2 = rep(0.20, n_scen * n_scen), rho_c = rep(0.0, n_scen * n_scen), n_t = 7L, n_c = 7L, a_t_00 = 0.25, a_t_01 = 0.25, a_t_10 = 0.25, a_t_11 = 0.25, a_c_00 = 0.25, a_c_01 = 0.25, a_c_10 = 0.25, a_c_11 = 0.25, m_t = NULL, m_c = NULL, theta_TV1 = 0.20, theta_MAV1 = 0.10, theta_TV2 = 0.20, theta_MAV2 = 0.10, theta_NULL1 = NULL, theta_NULL2 = NULL, z00 = NULL, z01 = NULL, z10 = NULL, z11 = NULL, xe_t_00 = NULL, xe_t_01 = NULL, xe_t_10 = NULL, xe_t_11 = NULL, xe_c_00 = NULL, xe_c_01 = NULL, xe_c_10 = NULL, xe_c_11 = NULL, alpha0e_t = NULL, alpha0e_c = NULL, nMC = 200L, CalcMethod = 'Exact', error_if_Miss = TRUE, Gray_inc_Miss = FALSE ) print(oc_ctrl) plot(oc_ctrl, base_size = 20) ## ----getgamma-ctrl, fig.width = 8, fig.height = 6----------------------------- res_gamma <- getgamma2bin( prob = 'posterior', design = 'controlled', GoRegions = 1L, NoGoRegions = 9L, pi_t1_go = 0.20, pi_t2_go = 0.20, rho_t_go = 0.0, pi_c1_go = 0.20, pi_c2_go = 0.20, rho_c_go = 0.0, pi_t1_nogo = 0.40, pi_t2_nogo = 0.40, rho_t_nogo = 0.0, pi_c1_nogo = 0.20, pi_c2_nogo = 0.20, rho_c_nogo = 0.0, target_go = 0.05, target_nogo = 0.20, n_t = 7L, n_c = 7L, a_t_00 = 0.25, a_t_01 = 0.25, a_t_10 = 0.25, a_t_11 = 0.25, a_c_00 = 0.25, a_c_01 = 0.25, a_c_10 = 0.25, a_c_11 = 0.25, theta_TV1 = 0.20, theta_MAV1 = 0.10, theta_TV2 = 0.20, theta_MAV2 = 0.10, theta_NULL1 = NULL, theta_NULL2 = NULL, m_t = NULL, m_c = NULL, z00 = NULL, z01 = NULL, z10 = NULL, z11 = NULL, xe_t_00 = NULL, xe_t_01 = NULL, xe_t_10 = NULL, xe_t_11 = NULL, xe_c_00 = NULL, xe_c_01 = NULL, xe_c_10 = NULL, xe_c_11 = NULL, alpha0e_t = NULL, alpha0e_c = NULL, nMC = 200L, gamma_go_grid = seq(0.05, 0.95, by = 0.05), gamma_nogo_grid = seq(0.05, 0.95, by = 0.05) ) plot(res_gamma, base_size = 20)