Calculate Pr(effect_dose - effect_reference_dose > EOI | data) or Pr(effect_dose > EOI | data).
pr_eoi(x, eoi, dose, reference_dose = NULL, time = NULL)output from a call to dreamer_mcmc().
a vector of the effects of interest (EOI) in the probability function.
a vector of the doses for which to calculate the posterior probabilities.
a vector of doses for relative effects of interest.
the time at which to calculate the posterior quantity. Defaults to the latest timepoint. Applies to longitudinal models only.
A tibble listing the doses, times, and
Pr(effect_dose - effect_reference_dose > eoi) if reference_dose
is specified; otherwise, Pr(effect_dose > eoi).
set.seed(888)
data <- dreamer_data_linear(
n_cohorts = c(20, 20, 20),
dose = c(0, 3, 10),
b1 = 1,
b2 = 3,
sigma = 5
)
# Bayesian model averaging
output <- dreamer_mcmc(
data = data,
n_adapt = 1e3,
n_burn = 1e3,
n_iter = 1e4,
n_chains = 2,
silent = FALSE,
mod_linear = model_linear(
mu_b1 = 0,
sigma_b1 = 1,
mu_b2 = 0,
sigma_b2 = 1,
shape = 1,
rate = .001,
w_prior = 1 / 2
),
mod_quad = model_quad(
mu_b1 = 0,
sigma_b1 = 1,
mu_b2 = 0,
sigma_b2 = 1,
mu_b3 = 0,
sigma_b3 = 1,
shape = 1,
rate = .001,
w_prior = 1 / 2
)
)
#> mod_linear
#> start : 2024-04-01 17:24:45.679
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 6
#> Unobserved stochastic nodes: 3
#> Total graph size: 50
#>
#> Initializing model
#>
#> finish: 2024-04-01 17:24:45.742
#> total : 0.06 secs
#> mod_quad
#> start : 2024-04-01 17:24:45.743
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 6
#> Unobserved stochastic nodes: 4
#> Total graph size: 59
#>
#> Initializing model
#>
#> finish: 2024-04-01 17:24:45.814
#> total : 0.07 secs
pr_eoi(output, dose = 3, eoi = 10)
#> # A tibble: 1 × 3
#> eoi dose prob
#> <dbl> <dbl> <dbl>
#> 1 10 3 0.262
# difference of two doses
pr_eoi(output, dose = 3, eoi = 10, reference_dose = 0)
#> # A tibble: 1 × 4
#> eoi dose reference_dose prob
#> <dbl> <dbl> <dbl> <dbl>
#> 1 10 3 0 0.0492
# single model
pr_eoi(output$mod_linear, dose = 3, eoi = 10)
#> # A tibble: 1 × 3
#> eoi dose prob
#> <dbl> <dbl> <dbl>
#> 1 10 3 0.257