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.
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.
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.
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)
#> exponential linear avgFit
#> med_reached 1 1 1
#> med 8 8 8
# 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)
#> avgFit exponential linear
#> med_reached 1 1 1
#> med 8 8 8