Do the posterior quantiles contain the true treatment effect?

Usage

sim_is_true_effect_covered(draws, true_effect, posterior_quantiles)

Arguments

draws: draws_array Object of class draws from CmdStanMCMC$draws().
true_effect: numeric. The true treatment effect.
posterior_quantiles: numeric. Vector of length two specifying quantiles of the posterior treatment effect distribution in which to search for the true effect.

Value

1L if the effect is contained within the quantiles, else 0L

Examples

base_mat <- matrix(
  c(
    rep(0, 200), rep(0, 200), rep(1, 200),
    rep(1, 200), rep(0, 200), rep(0, 200),
    rep(0, 600)
  ),
  ncol = 3,
  dimnames = list(NULL, c("ext", "trt", "driftOR"))
)

add_binary_endpoint <- function(odds_ratio,
                                base_matrix = base_mat) {
  linear_predictor <- base_matrix[, "trt"] * log(odds_ratio)
  prob <- 1 / (1 + exp(-linear_predictor))

  bin_endpoint <- rbinom(
    NROW(base_matrix),
    1,
    prob
  )

  cbind(base_matrix, matrix(bin_endpoint, ncol = 1, dimnames = list(NULL, "ep")))
}

data_list <- list(
  list(add_binary_endpoint(1.5), add_binary_endpoint(1.5)),
  list(add_binary_endpoint(2.5), add_binary_endpoint(2.5))
)

guide <- data.frame(
  trueOR = c(1.5, 2.5),
  driftOR = c(1.0, 1.0),
  index = 1:2
)

sdl <- sim_data_list(
  data_list = data_list,
  guide = guide,
  effect = "trueOR",
  drift = "driftOR",
  index = "index"
)

x <- create_simulation_obj(
  data_matrix_list = sdl,
  outcome = logistic_bin_outcome("ep", normal_prior(0, 1000)),
  borrowing = sim_borrowing_list(list(
    full_borrowing = borrowing_details("Full borrowing", "ext"),
    bdb = borrowing_details("BDB", "ext", exponential_prior(0.0001))
  )),
  treatment = treatment_details("trt", normal_prior(0, 1000))
)

if (FALSE) {
i <- 1
j <- 1
true_effect <- x@guide[i, x@data_matrix_list@effect]
anls_obj <- x@analysis_obj_list[[i]][[j]]
res <- mcmc_sample(anls_obj, iter_sampling = 500)
draws <- res$draws()

psborrow2:::sim_is_true_effect_covered(
  draws,
  true_effect,
  c(0.025, 0.975)
)
}