# \donttest{
set.seed(123)
## Examples for binary endpoints
## Suppose that the informative prior constructed based on historical data is
## beta(40, 60)
prior.historical <- RBesT::mixbeta(c(1, 40, 60))
## Data of the control arm
data.control <- stats::rbinom(60, size = 1, prob = 0.42)
## Calculate the mixture weight of the SAM prior
wSAM <- SAM_weight(if.prior = prior.historical,
delta = 0.15, ## Clinically significant difference
data = data.control ## Control arm data
)
## Assume beta(1,1) as the non-informative prior used for mixture
nf.prior <- RBesT::mixbeta(nf.prior = c(1,1,1))
## Generate the SAM prior
SAM.prior <- SAM_prior(if.prior = prior.historical, ## Informative prior
nf.prior = nf.prior, ## Non-informative prior
weight = wSAM ## Mixture weight of the SAM prior
)
graphics::plot(SAM.prior)
## Examples for continuous endpoints
## Suppose that the informative prior constructed based on historical data is
## N(0, 3)
sigma <- 3
prior.mean <- 0
prior.se <- sigma/sqrt(100)
prior.historical <- RBesT::mixnorm(c(1, prior.mean, prior.se), sigma = sigma)
## Data of the control arm
data.control <- stats::rnorm(80, mean = 0, sd = sigma)
## Calculate the mixture weight of the SAM prior
wSAM <- SAM_weight(if.prior = prior.historical,
delta = 0.2 * sigma, ## Clinically significant difference
data = data.control ## Control arm data
)
## Assume unit-information prior N(0,3) as the non-informative prior used
## for the mixture
nf.prior <- RBesT::mixnorm(nf.prior = c(1,prior.mean, sigma),
sigma = sigma)
## Generate the SAM prior
SAM.prior <- SAM_prior(if.prior = prior.historical, ## Informative prior
nf.prior = nf.prior, ## Non-informative prior
weight = wSAM ## Mixture weight of the SAM prior
)
graphics::plot(SAM.prior)
# }
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