Compile MCMC sampler using STAN and create simulation object

Usage

create_simulation_obj(
  data_matrix_list,
  covariate = NULL,
  outcome,
  borrowing,
  treatment,
  quiet = TRUE
)

Arguments

data_matrix_list: SimDataList. The list of lists of data matrices created with sim_data_list().
covariate: SimCovariateList or Covariate or NULL. List of Covariate objects created with sim_covariate(), a single Covariate object created by add_covariates(), or NULL (no covariate adjustment).
outcome: SimOutcomeList or Outcome. List of Outcome objects created with sim_outcome(), or single Outcome object (e.g., created by exp_surv_dist()).
borrowing: SimBorrowingList or Borrowing. List of Borrowing objects created with sim_borrowing(), or a single Borrowing object created by borrowing_details().
treatment: SimTreatmentList or Treatment. List of Treatment objects created with sim_treatment() or a single Treatment object created by treatment_details().
quiet: logical. Whether to print messages (quiet = FALSE) or not (quiet = TRUE, the default)

Value

Object of class Simulation.

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"
)

sim_object <- 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))
)