Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- message=FALSE, warning=FALSE--------------------------------------------
library(trialr)
## -----------------------------------------------------------------------------
target <- 0.25
skeleton <- c(0.05, 0.15, 0.25, 0.4, 0.6)
## -----------------------------------------------------------------------------
outcomes <- '2NN 3NN 4TT'
## ---- results = "hide", warning=FALSE, message=FALSE--------------------------
fit1 <- stan_crm(outcomes, skeleton = skeleton, target = target,
model = 'empiric', beta_sd = sqrt(1.34), seed = 123)
## -----------------------------------------------------------------------------
fit1
## -----------------------------------------------------------------------------
prob_tox_samp1 <- as.data.frame(fit1, 'prob_tox')
prob_tox1 <- colMeans(prob_tox_samp1)
prob_tox1
## -----------------------------------------------------------------------------
prob_too_toxic1 <- unname(colMeans(prob_tox_samp1 > target))
prob_too_toxic1
## ---- results = "hide", warning=FALSE, message=FALSE--------------------------
fit2 <- stan_crm(outcomes, skeleton = skeleton, target = target,
model = 'logistic', a0 = 3, beta_mean = 0, beta_sd = sqrt(1.34),
seed = 123)
## -----------------------------------------------------------------------------
fit2
## ---- results = "hide", warning=FALSE, message=FALSE--------------------------
fit3 <- stan_crm(outcomes, skeleton = skeleton, target = target,
model = 'logistic_gamma',
a0 = 3, beta_shape = 1, beta_inverse_scale = 1,
seed = 123)
## -----------------------------------------------------------------------------
fit3
## ---- results = "hide", warning=FALSE, message=FALSE--------------------------
fit4 <- stan_crm(outcomes, skeleton = skeleton, target = target,
model = 'logistic2',
alpha_mean = 0, alpha_sd = 1, beta_mean = 0, beta_sd = 1,
seed = 123)
## -----------------------------------------------------------------------------
fit4
## -----------------------------------------------------------------------------
post_curves <- data.frame(
DoseLevel = 1:length(skeleton),
Empiric = fit1$prob_tox,
Logit1N = fit2$prob_tox,
Logit1G = fit3$prob_tox,
Logit2N = fit4$prob_tox
)
knitr::kable(post_curves, digits = 2)
## ---- fig.width=7, fig.height=7-----------------------------------------------
post_curves_tall <- data.frame(
DoseLevel = rep(1:length(skeleton), times = 4),
ProbTox = c(fit1$prob_tox, fit2$prob_tox, fit3$prob_tox, fit4$prob_tox),
Model = rep(c('Empiric', 'Logit1N', 'Logit1G', 'Logit2N'), each = 5)
)
library(ggplot2)
ggplot(post_curves_tall, aes(x = DoseLevel, y = ProbTox, group = Model, col = Model)) +
geom_line(size = 1.2) +
geom_hline(yintercept = target, col = 'red', linetype = 'dashed')
## -----------------------------------------------------------------------------
knitr::kable(rstan::summary(fit4$fit, 'alpha')$summary, digits = 2)
## -----------------------------------------------------------------------------
knitr::kable(
data.frame(
DoseLevel = 1:length(skeleton),
ProbMTD_Empiric = as.numeric(fit1$prob_mtd),
ProbMTD_Logit2N = as.numeric(fit4$prob_mtd)
), digits = 2
)
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