eval_scenario_cont_2arm: Evaluate One Scenario for a Two-Arm Comparative Trial with...

In SAMprior: Self-Adapting Mixture (SAM) Priors

Evaluate One Scenario for a Two-Arm Comparative Trial with Continuous Endpoint

Description

The eval_scenario_cont_2arm function is designed to evaluate repeated-sampling operating characteristics for a two-arm comparative trial with a continuous endpoint under one borrowing strategy: self-adapting mixture prior (SAM), robust MAP prior with fixed weight (rMAP), or non-informative prior (NP).

Usage

eval_scenario_cont_2arm(
  if.prior,
  nf.prior,
  prior.t = nf.prior,
  n.t,
  n,
  sigma.t,
  sigma,
  theta.t,
  theta,
  cutoff,
  delta,
  method = c("SAM", "rMAP", "NP"),
  alternative = c("greater", "less"),
  margin = 0,
  weight_rMAP = 0.5,
  method.w = "LRT",
  prior.odds = 1,
  rel.tol = 1e-06,
  n_sd_int = 8
)

Arguments

if.prior	Informative prior constructed based on historical data for the control arm, represented (approximately) as a normal mixture prior.
nf.prior	Non-informative prior used as the robustifying component for the control arm prior.
prior.t	Prior used for the treatment arm. If missing, the default value is set to be nf.prior.
n.t	Number of subjects in the treatment arm.
n	Number of subjects in the control arm.
sigma.t	Known sampling standard deviation in the treatment arm.
sigma	Known sampling standard deviation in the control arm.
theta.t	True treatment arm mean.
theta	True control arm mean.
cutoff	Posterior probability cutoff used for decision making. Rejection occurs if the posterior probability exceeds cutoff.
delta	Clinically significant difference used for the SAM prior. This argument is only used when method = "SAM".
method	Borrowing strategy for the control arm. Must be one of "SAM", "rMAP", or "NP".
alternative	Direction of the posterior decision. Must be one of "greater" (for superiority) or "less" (for inferiority).
margin	Clinical margin. Must be a non-negative scalar. The default value is 0.
weight_rMAP	Weight assigned to the informative prior component (0 \leq weight_rMAP \leq 1) for the robust MAP prior. This argument is only used when method = "rMAP". The default value is 0.5.
method.w	Methods used to determine the mixture weight for SAM priors. The default method is "LRT" (Likelihood Ratio Test), the alternative option is "PPR" (Posterior Probability Ratio). See SAM_weight for more details.
prior.odds	The prior probability of H_0 being true compared to the prior probability of H_1 being true using PPR method. The default value is 1. See SAM_weight for more details.
rel.tol	Relative tolerance passed to numerical integration.
n_sd_int	Half-width of the numerical integration region for each arm, expressed as a multiple of the corresponding standard error.

Details

The treatment effect is defined as \tau = \theta_t - \theta, where \theta_t and \theta denote the true means in the treatment and control arms, respectively.

For a given true scenario (\theta_t, \theta), this function computes the repeated-sampling rejection probability, bias, root mean squared error (RMSE), and mean borrowing weight using one-dimensional numerical integration.

The rejection probability is computed by reducing the repeated-sampling decision rule to a one-dimensional integral over the control-arm sample mean.

Bias and RMSE are evaluated for the posterior mean estimator of the control arm mean \theta. Both are computed from one-dimensional first and second moments of the control-arm posterior mean. The mean borrowing weight is computed by one-dimensional integration over the control-arm sample mean.

Under null scenarios, reject_prob corresponds to type I error. Under alternative scenarios, it corresponds to power.

Value

A one-row data frame with the following columns:

theta

True control arm mean.

theta.t

True treatment arm mean.

delta_true

True treatment effect, \tau = \theta_t - \theta.

method

Borrowing method used.

alternative

Direction of the posterior decision.

cutoff

Posterior probability cutoff used for decision making.

margin

Clinical margin used for inference.

reject_prob

Repeated-sampling rejection probability.

bias

Bias of the posterior mean estimator of \theta.

rmse

Root mean squared error of the posterior mean estimator of \theta.

mean_weight

Average borrowing weight under the specified method.

SAMprior documentation built on April 28, 2026, 1:07 a.m.