getMED: getMED

In BayesianMCPMod: Simulate, Evaluate, and Analyze Dose Finding Trials with Bayesian MCPMod

getMED

Description

This function provides information on the minimally efficacious dose (MED). The MED evaluation can either be based on the fitted model shapes (model_fits) or on bootstrapped quantiles (bs_quantiles).

Usage

getMED(
  delta,
  evidence_level = 0.5,
  dose_levels = NULL,
  model_fits = NULL,
  bs_quantiles = NULL
)

Arguments

delta	A numeric value for the threshold Delta.
evidence_level	A numeric value between 0 and 1 for the evidence level gamma. Used for the bs_quantiles-based evaluation and not used for the model_fits-based evaluation. Default 0.5.
dose_levels	A vector of numerics containing the different dosage levels. Default NULL.
model_fits	An object of class modelFits as created with getModelFits(). Default NULL.
bs_quantiles	A dataframe created with getBootstrapQuantiles(). Default NULL.

Details

The function assumes that the 1st dose group is the control dose group.

The bootstrap approach allows for an MED based on decision rules of the form

\widehat{\text{MED}} = \text{arg min}_{d\in\{d_1, \dots, d_k\}} \left\{ \text{Pr}\left(f(d, \hat\theta) - f(d_1, \hat\theta) > \Delta\right) > \gamma \right\} .

The model-shape approach takes the point estimate of the model into account.

Value

A matrix with rows for MED reached, MED, and MED index in the vector of dose levels and columns for the dose-response shapes.

Examples

posterior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 1), sigma = 2),
                       DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 3, s = 1.2), sigma = 2),
                       DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) ,
                       DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 6, s = 1.2), sigma = 2) ,
                       DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 6.5, s = 1.1), sigma = 2))
models         <- c("exponential", "linear")
dose_levels    <- c(0, 1, 2, 4, 8)
model_fits     <- getModelFits(models      = models,
                               posterior   = posterior_list,
                               dose_levels = dose_levels,
                               simple      = TRUE)

# MED based on the model_fit:
getMED(delta = 5, model_fits = model_fits)
                               
# MED based on bootstrapped quantiles
bs_quantiles <- getBootstrapQuantiles(model_fits = model_fits,
                                      quantiles  = c(0.025, 0.2, 0.5),
                                      n_samples  = 100) # speeding up example run time
                                      
getMED(delta          = 5,
       evidence_level = 0.8,
       bs_quantiles   = bs_quantiles)

BayesianMCPMod documentation built on April 4, 2025, 5:24 a.m.