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inst/doc/two-binary.R
In BayesianQDM: Bayesian Quantitative Decision-Making Framework for Binary and Continuous Endpoints
## ----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('
<table style="border-collapse:collapse; text-align:center; font-size:0.9em;">
<caption>Nine-region grid for two-endpoint posterior probability</caption>
<thead>
<tr>
<th colspan="2" rowspan="2" style="border:1px solid #aaa; background:
linear-gradient(to top right, white 49.5%, #aaa 49.5%, #aaa 50.5%, white 50.5%);
min-width:80px; min-height:60px; padding:4px;">
</th>
<th colspan="3" style="border:1px solid #aaa; padding:6px; font-weight:normal;">Endpoint 1</th>
</tr>
<tr>
<th style="border:1px solid #aaa; padding:6px; font-weight:normal;">θ<sub>1</sub> > θ<sub>TV1</sub></th>
<th style="border:1px solid #aaa; padding:6px; font-weight:normal;">θ<sub>TV1</sub> ≥ θ<sub>1</sub> > θ<sub>MAV1</sub></th>
<th style="border:1px solid #aaa; padding:6px; font-weight:normal;">θ<sub>MAV1</sub> ≥ θ<sub>1</sub></th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="3" style="border:1px solid #aaa; padding:6px; writing-mode:vertical-rl;
transform:rotate(180deg);">Endpoint 2</td>
<td style="border:1px solid #aaa; padding:6px; text-align:left;">
θ<sub>2</sub> > θ<sub>TV2</sub></td>
<td style="border:1px solid #aaa; padding:6px;">R1</td>
<td style="border:1px solid #aaa; padding:6px;">R4</td>
<td style="border:1px solid #aaa; padding:6px;">R7</td>
</tr>
<tr>
<td style="border:1px solid #aaa; padding:6px; text-align:left;">
θ<sub>TV2</sub> ≥ θ<sub>2</sub> > θ<sub>MAV2</sub></td>
<td style="border:1px solid #aaa; padding:6px;">R2</td>
<td style="border:1px solid #aaa; padding:6px;">R5</td>
<td style="border:1px solid #aaa; padding:6px;">R8</td>
</tr>
<tr>
<td style="border:1px solid #aaa; padding:6px; text-align:left;">
θ<sub>MAV2</sub> ≥ θ<sub>2</sub></td>
<td style="border:1px solid #aaa; padding:6px;">R3</td>
<td style="border:1px solid #aaa; padding:6px;">R6</td>
<td style="border:1px solid #aaa; padding:6px;">R9</td>
</tr>
</tbody>
</table>
')
## ----four-region-bin, echo = FALSE, results = 'asis'--------------------------
cat('
<table style="border-collapse:collapse; text-align:center; font-size:0.9em;">
<caption>Four-region grid for two-endpoint predictive probability</caption>
<thead>
<tr>
<th colspan="2" rowspan="2" style="border:1px solid #aaa; background:
linear-gradient(to top right, white 49.5%, #aaa 49.5%, #aaa 50.5%, white 50.5%);
min-width:80px; min-height:60px; padding:4px;">
</th>
<th colspan="2" style="border:1px solid #aaa; padding:6px; font-weight:normal;">Endpoint 1</th>
</tr>
<tr>
<th style="border:1px solid #aaa; padding:6px; font-weight:normal;">θ<sub>1</sub> > θ<sub>NULL1</sub></th>
<th style="border:1px solid #aaa; padding:6px; font-weight:normal;">θ<sub>1</sub> ≤ θ<sub>NULL1</sub></th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="2" style="border:1px solid #aaa; padding:6px; writing-mode:vertical-rl;
transform:rotate(180deg);">Endpoint 2</td>
<td style="border:1px solid #aaa; padding:6px; text-align:left;">
θ<sub>2</sub> > θ<sub>NULL2</sub></td>
<td style="border:1px solid #aaa; padding:6px;">R1</td>
<td style="border:1px solid #aaa; padding:6px;">R3</td>
</tr>
<tr>
<td style="border:1px solid #aaa; padding:6px; text-align:left;">
θ<sub>2</sub> ≤ θ<sub>NULL2</sub></td>
<td style="border:1px solid #aaa; padding:6px;">R2</td>
<td style="border:1px solid #aaa; padding:6px;">R4</td>
</tr>
</tbody>
</table>
')
## ----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)
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## ----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('
<table style="border-collapse:collapse; text-align:center; font-size:0.9em;">
<caption>Nine-region grid for two-endpoint posterior probability</caption>
<thead>
<tr>
<th colspan="2" rowspan="2" style="border:1px solid #aaa; background:
linear-gradient(to top right, white 49.5%, #aaa 49.5%, #aaa 50.5%, white 50.5%);
min-width:80px; min-height:60px; padding:4px;">
</th>
<th colspan="3" style="border:1px solid #aaa; padding:6px; font-weight:normal;">Endpoint 1</th>
</tr>
<tr>
<th style="border:1px solid #aaa; padding:6px; font-weight:normal;">θ<sub>1</sub> > θ<sub>TV1</sub></th>
<th style="border:1px solid #aaa; padding:6px; font-weight:normal;">θ<sub>TV1</sub> ≥ θ<sub>1</sub> > θ<sub>MAV1</sub></th>
<th style="border:1px solid #aaa; padding:6px; font-weight:normal;">θ<sub>MAV1</sub> ≥ θ<sub>1</sub></th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="3" style="border:1px solid #aaa; padding:6px; writing-mode:vertical-rl;
transform:rotate(180deg);">Endpoint 2</td>
<td style="border:1px solid #aaa; padding:6px; text-align:left;">
θ<sub>2</sub> > θ<sub>TV2</sub></td>
<td style="border:1px solid #aaa; padding:6px;">R1</td>
<td style="border:1px solid #aaa; padding:6px;">R4</td>
<td style="border:1px solid #aaa; padding:6px;">R7</td>
</tr>
<tr>
<td style="border:1px solid #aaa; padding:6px; text-align:left;">
θ<sub>TV2</sub> ≥ θ<sub>2</sub> > θ<sub>MAV2</sub></td>
<td style="border:1px solid #aaa; padding:6px;">R2</td>
<td style="border:1px solid #aaa; padding:6px;">R5</td>
<td style="border:1px solid #aaa; padding:6px;">R8</td>
</tr>
<tr>
<td style="border:1px solid #aaa; padding:6px; text-align:left;">
θ<sub>MAV2</sub> ≥ θ<sub>2</sub></td>
<td style="border:1px solid #aaa; padding:6px;">R3</td>
<td style="border:1px solid #aaa; padding:6px;">R6</td>
<td style="border:1px solid #aaa; padding:6px;">R9</td>
</tr>
</tbody>
</table>
')
## ----four-region-bin, echo = FALSE, results = 'asis'--------------------------
cat('
<table style="border-collapse:collapse; text-align:center; font-size:0.9em;">
<caption>Four-region grid for two-endpoint predictive probability</caption>
<thead>
<tr>
<th colspan="2" rowspan="2" style="border:1px solid #aaa; background:
linear-gradient(to top right, white 49.5%, #aaa 49.5%, #aaa 50.5%, white 50.5%);
min-width:80px; min-height:60px; padding:4px;">
</th>
<th colspan="2" style="border:1px solid #aaa; padding:6px; font-weight:normal;">Endpoint 1</th>
</tr>
<tr>
<th style="border:1px solid #aaa; padding:6px; font-weight:normal;">θ<sub>1</sub> > θ<sub>NULL1</sub></th>
<th style="border:1px solid #aaa; padding:6px; font-weight:normal;">θ<sub>1</sub> ≤ θ<sub>NULL1</sub></th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="2" style="border:1px solid #aaa; padding:6px; writing-mode:vertical-rl;
transform:rotate(180deg);">Endpoint 2</td>
<td style="border:1px solid #aaa; padding:6px; text-align:left;">
θ<sub>2</sub> > θ<sub>NULL2</sub></td>
<td style="border:1px solid #aaa; padding:6px;">R1</td>
<td style="border:1px solid #aaa; padding:6px;">R3</td>
</tr>
<tr>
<td style="border:1px solid #aaa; padding:6px; text-align:left;">
θ<sub>2</sub> ≤ θ<sub>NULL2</sub></td>
<td style="border:1px solid #aaa; padding:6px;">R2</td>
<td style="border:1px solid #aaa; padding:6px;">R4</td>
</tr>
</tbody>
</table>
')
## ----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)
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