Performs Bayesian MCP Test step and modeling in a combined fashion. See performBayesianMCP() function for MCP Test step and getModelFits() for the modeling step
Usage
performBayesianMCPMod(
posterior_list,
contr,
crit_prob_adj,
simple = FALSE,
avg_fit = TRUE,
delta = NULL,
evidence_level = NULL,
med_selection = c("avgFit", "bestFit"),
n_samples = 1000
)Arguments
- posterior_list
An object of class 'postList' as created by getPosterior() containing information about the (mixture) posterior distribution per dose group
- contr
An object of class 'optContr' as created by the getContr() function. It contains the contrast matrix to be used for the testing step.
- crit_prob_adj
A getCritProb object, specifying the critical value to be used for the testing (on the probability scale).
- simple
Boolean variable, defining whether simplified fit will be applied. Passed to the getModelFits() function. Default FALSE.
- avg_fit
Boolean variable, defining whether an average fit (based on generalized AIC weights) should be performed in addition to the individual models. Default TRUE.
- delta
A numeric value for the threshold Delta for the MED assessment. If NULL, no MED assessment is performed. Default NULL.
- evidence_level
A numeric value between 0 and 1 for the evidence level gamma for the MED assessment. Only required for Bayesian MED assessment, see ?getMED for details. Default NULL.
- med_selection
A string, either "avgFit" or "bestFit" based on the lowest gAIC, for the method of MED selection. Default "avgFit".
- n_samples
A numerical for the number of bootstrapped samples in case the Bayesian MED assessment is performed. Default 1e3.
Examples
mods <- DoseFinding::Mods(linear = NULL,
emax = c(0.5, 1.2),
exponential = 2,
doses = c(0, 0.5, 2,4, 8))
dose_levels <- c(0, 0.5, 2, 4, 8)
sd_posterior <- c(2.8, 3, 2.5, 3.5, 4)
contr_mat <- getContr(
mods = mods,
dose_levels = dose_levels,
sd_posterior = sd_posterior)
critVal <- getCritProb(
mods = mods,
dose_weights = c(50, 50, 50, 50, 50), #reflecting the planned sample size
dose_levels = dose_levels,
alpha_crit_val = 0.6) # unreasonable alpha for this example, rather choose 0.05
prior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 5), sigma = 2),
DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 1, s = 12), sigma = 2),
DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 1.2, s = 11), sigma = 2) ,
DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 1.3, s = 11), sigma = 2) ,
DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 2, s = 13), sigma = 2))
mu <- c(0, 1, 1.5, 2, 2.5)
S_hat <- c(5, 4, 6, 7, 8)
posterior_list <- getPosterior(
prior_list = prior_list,
mu_hat = mu,
S_hat = S_hat,
calc_ess = TRUE)
performBayesianMCPMod(posterior_list = posterior_list,
contr = contr_mat,
crit_prob_adj = critVal,
simple = FALSE,
delta = 1)
#> Bayesian Multiple Comparison Procedure
#> Significant: 1
#> Critical Probability: 0.5702732
#> Maximum Posterior Probability: 0.6412959
#> Posterior Probabilities for Model Shapes
#> lin emax1 emax2 exp
#> Posterior Prob 0.6191201 0.6412959 0.6410572 0.5839261
#> Average Posterior ESS
#> Dose Level: Ctrl DG_1 DG_2 DG_3 DG_4
#> Avg Post ESS: 0.3 0.3 0.1 0.1 0.1
#> MED Assessment
#> Selection Method: avgFit
#> Identification Rate: 1
#> Dose Level: 0.5 2.0 4.0 8.0
#> MED Freq: 0 0 1 0
#> MED not reached Freq: 0
#> No success in MCP step Freq: 0
#> Model Coefficients
#> lin e0 = 0.5, delta = 0.3
#> emax e0 = 0, eMax = 2.2, ed50 = 0.8
#> exp e0 = 0.5, e1 = 3.4, delta = 16
#> Dose Levels
#> Ctrl = 0, DG_1 = 0.5, DG_2 = 2, DG_3 = 4, DG_4 = 8
#> Predictions, Maximum Effect, gAIC, Model Weights & Significance
#> Ctrl DG_1 DG_2 DG_3 DG_4 mEff gAIC w Sign
#> lin 0.5 0.6 1.0 1.6 2.7 2.3 4.0 0.6 1.0
#> emax 0.0 0.9 1.6 1.9 2.1 2.0 6.0 0.2 1.0
#> exp 0.5 0.6 1.0 1.5 2.7 2.2 6.0 0.2 1.0
#> avgFit 0.4 0.7 1.2 1.6 2.6 2.2 NA NA 1.0