Runs Monte Carlo simulations of an E vs C2 trial and performs
Bayesian analysis with a NAP-based prior constructed by NAP_prior().
The routine supports both single external study setting and multiple external studies
settings as encoded in the provided NAP_prior object, and works with either a fixed mixture
weight (mNAP) or an elastic, data-adaptive weight (eNAP).
NAP_oc(
NAP_prior = NULL,
theta_EC2 = 0,
n_EC2 = 200,
lambda = 2,
sim_model = c("Exponential", "Weibull"),
model_param = 0.05,
iter = 2000,
chains = 4,
seed = 123,
nsim = 100,
jags_model = NULL
)A data frame with one row per replicate containing:
post_mean, post_sd, low95, hi95
— posterior mean, SD, and 95\
prob_E_better — posterior probability theta_{E,C2} < 0.
prior_weight, post_weight — prior and updated weights
used in the mixture (for eNAP, prior_weight is w(Z)).
sigma_hat — posterior mean of between-study SD (RE only; NA for FE).
An object returned by NAP_prior() that contains
the prior specification and (for eNAP) any calibrated tuning parameters a, b.
Numeric scalar. True log-hazard ratio for E vs C2
used to generate the direct trial data.
Integer. Total sample size for the simulated E vs C2 trial.
Numeric scalar \(> 0\). Randomization ratio E:C2; e.g.,
lambda = 2 means 2:1 allocation to E:C2.
Character string. Event-time model used to simulate individual
times; one of "Exponential" or "Weibull".
Named numeric vector for the baseline hazard of the control arm.
For sim_model = "Exponential", use c(rate = ...).
For sim_model = "Weibull", use c(shape = ..., rate = ...).
Integer. Total MCMC iterations per chain for JAGS (default 2000).
Integer. Number of MCMC chains (default 4).
Integer. Random seed for the simulation replicates.
Integer. Number of Monte Carlo replicates (default 100).
Either a length-1 character string containing JAGS model code
(e.g., a packaged object such as jags_model_RE) or a file path to a
.txt JAGS model. If NULL, a default FE/RE model is chosen to
match the NAP_prior mode.