assessDesign

Description

This function performs simulation based trial design evaluations for a set of specified dose-response models

Usage

assessDesign(
  n_patients,
  mods,
  prior_list,
  sd,
  n_sim = 1000,
  alpha_crit_val = 0.05,
  modeling = FALSE,
  simple = TRUE,
  avg_fit = TRUE,
  reestimate = FALSE,
  contr = NULL,
  dr_means = NULL,
  delta = NULL,
  evidence_level = NULL,
  med_selection = c("avgFit", "bestFit")
)

Arguments

Value

Returns success probabilities for the different assumed dose-response shapes, attributes also includes information around average success rate (across all assumed models) and prior Effective sample size

Examples

mods <- DoseFinding::Mods(linear      = NULL,
                          emax        = c(0.5, 1.2),
                          exponential = 2,
                          doses       = c(0, 0.5, 2,4, 8),
                          maxEff      = 6)
sd <- 12
prior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 12), 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))
n_patients <- c(40, 60, 60, 60, 60)

success_probabilities <- assessDesign(
  n_patients  = n_patients,
  mods        = mods,
  prior_list  = prior_list,
  sd          = sd,
  n_sim       = 1e2) # speed up example run time

success_probabilities

if (interactive()) { # takes typically > 5 seconds

# with MED estimation without bootstrapping
# see ?getMED for details

success_probabilities <- assessDesign(
  n_patients     = n_patients,
  mods           = mods,
  prior_list     = prior_list,
  sd             = sd,
  modeling       = TRUE,
  n_sim          = 10, # speed up example run time
  delta          = 7)
  
  success_probabilities
  
# with MED estimation with bootstrapping

success_probabilities <- assessDesign(
  n_patients     = n_patients,
  mods           = mods,
  prior_list     = prior_list,
  sd             = sd,
  modeling       = TRUE,
  n_sim          = 10, # speed up example run time
  delta          = 7,
  evidence_level = 0.8)
  
  success_probabilities

}

getBootstrapQuantiles

Description

A function for the calculation of bootstrapped model predictions. Samples from the posterior distribution are drawn (via the RBesT function rmix()) and for every sample the simplified fitting step (see getModelFits() function) and a prediction is performed. These fits are then used to identify the specified quantiles. This approach can be considered as the Bayesian equivalent of the frequentist bootstrap approach described in O'Quigley et al. (2017). Instead of drawing n bootstrap samples from the sampling distribution of the trial dose-response estimates, here the samples are directly taken from the posterior distribution.

Usage

getBootstrapQuantiles(model_fits, quantiles, n_samples = 1000, doses = NULL)

Arguments

Value

A data frame with columns for model, dose, and bootstrapped samples

References

O'Quigley J, Iasonos A, Bornkamp B. 2017. Handbook of Methods for Designing, Monitoring, and Analyzing Dose-Finding Trials (1st ed.). Chapman and Hall/CRC. doi:10.1201/9781315151984

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)

bs_quantiles <- getBootstrapQuantiles(model_fits = model_fits,
                                      quantiles  = c(0.025, 0.5, 0.8, 0.975),
                                      n_samples  = 10, # speeding up example run time
                                      doses      = c(0, 6, 8))
                      
bs_quantiles

getBootstrapSamples

Description

A function to return bootstrap samples from the fitted dose-response models. Samples from the posterior distribution are drawn (via the RBesT function rmix()) and for every sample the simplified fitting step (see getModelFits() function) and a prediction is performed. These samples are returned by this function. This approach can be considered as the Bayesian equivalent of the frequentist bootstrap approach described in O'Quigley et al. (2017). Instead of drawing n bootstrap samples from the sampling distribution of the trial dose-response estimates, here the samples are directly taken from the posterior distribution.

Usage

getBootstrapSamples(model_fits, n_samples = 1000, doses = NULL)

Arguments

Value

A data frame with entries model, dose, and sample

References

O'Quigley J, Iasonos A, Bornkamp B. 2017. Handbook of Methods for Designing, Monitoring, and Analyzing Dose-Finding Trials (1st ed.). Chapman and Hall/CRC. doi:10.1201/9781315151984

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)
                           
bs_samples <- getBootstrapSamples(model_fits = model_fits,
                                  n_samples  = 10, # speeding up example run time
                                  doses      = c(0, 6, 8))
                      
bs_samples

getContr

Description

This function calculates contrast vectors that are optimal for detecting certain alternatives via applying the function optContr() of the DoseFinding package. Hereby, 4 different options can be distinguished that are automatically executed based on the input that is provided

1. Bayesian approach: If dose_weights and a prior_list are provided an optimized contrasts for the posterior sample size is calculated. In detail, in a first step the dose_weights (typically the number of patients per dose group) and the prior information is combined by calculating for each dose group a posterior effective sample. Based on this posterior effective sample sizes the allocation ratio is derived, which allows for a calculation on pseudo-optimal contrasts via regular MCPMod are calculated from the regular MCPMod for these specific weights

2. Frequentist approach: If only dose_weights are provided optimal contrast vectors are calculated from the regular MCPMod for these specific weights

3. Bayesian approach + re-estimation: If only a sd_posterior (i.e. variability of the posterior distribution) is provided, pseudo-optimal contrasts based on these posterior weights will be calculated

4. Frequentist approach+re-estimation: If only a se_new_trial (i.e. the estimated variability per dose group of a new trial) is provided, optimal contrast vectors are calculated from the regular MCPMod for this specific vector of standard errors. For the actual evaluation this vector of standard errors is translated into a (diagonal) matrix of variances

Usage

getContr(
  mods,
  dose_levels,
  dose_weights = NULL,
  prior_list = NULL,
  sd_posterior = NULL,
  se_new_trial = NULL
)

Arguments

Value

An object of class 'optContr' as provided by the function 'DoseFinding::optContr()'.

Examples

dose_levels  <- c(0, 0.5, 2, 4, 8)
mods <- DoseFinding::Mods(
  linear      = NULL,
  emax        = c(0.5, 1.2),
  exponential = 2,
  doses       = dose_levels,
  maxEff      = 6)
sd_posterior <- c(2.8, 3, 2.5, 3.5, 4)

contr_mat <- getContr(
  mods         = mods,
  dose_levels  = dose_levels,
  sd_posterior = sd_posterior)

getCritProb

Description

This function calculates multiplicity adjusted critical values. The critical values are calculated in such a way that when using non-informative priors the actual error level for falsely declaring a significant trial in the Bayesian MCPMod is controlled (by the specified alpha level). Hereby optimal contrasts of the frequentist MCPMod are applied and two options can be distinguished

1. Frequentist approach: If only dose_weights are provided optimal contrast vectors are calculated from the regular MCPMod for these specific weights and the corresponding critical value for this set of contrasts is calculated via the critVal() function of the DoseFinding package.

2. Frequentist approach + re-estimation: If only a se_new_trial (i.e. the estimated variability per dose group of a new trial) is provided, optimal contrast vectors are calculated from the regular MCPMod for this specific vector of standard errors. Here as well the critical value for this set of contrasts is calculated via the critVal() function of the DoseFinding package.

Usage

getCritProb(
  mods,
  dose_levels,
  dose_weights = NULL,
  se_new_trial = NULL,
  alpha_crit_val = 0.025
)

Arguments

Value

Multiplicity adjusted critical value on the probability scale.

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)
critVal <- getCritProb(
  mods           = mods,
  dose_weights   = c(50,50,50,50,50), #reflecting the planned sample size
  dose_levels    = dose_levels,
  alpha_crit_val = 0.05)

getESS

Description

This function calculates the effective sample size for every dose group via the RBesT function ess().

Usage

getESS(post_list)

Arguments

Value

A vector of the effective sample sizes for each dose group

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

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)

getModelFits

Description

Fits dose-response curves for the specified dose-response models, based on the posterior distributions. For the simplified fit, multivariate normal distributions will be approximated and reduced by one-dimensional normal distributions. For the default case, the Nelder-Mead algorithm is used. In detail, for both approaches the mean vector \theta^{Y} and the covariance \Sigma of the (mixture) posterior distributions and the corresponding posterior weights \tilde{\omega}_{l} for l \in {1,...,L} are used as basis For the full fit a GLS estimator is used to minimize the following expression for the respective dose-response models m

\hat{\theta}_{m}=\text{arg min}_{\theta_{m}} \sum_{l=1}^{L} \tilde{\omega}_{l}(\theta_{l_{i}}^{Y}-f(dose_{i},\hat{\theta}_{m}))'\Sigma_{l}^{-1}(\theta_{l_{i}}^{Y}-f(dose_{i},\hat{\theta}_{m}))

Therefore the function nloptr of the nloptr package is utilized. In the simplified case L=1, as the dimension of the posterior is reduced to 1 first. The generalized AIC values are calculated via the formula

gAIC_{m} = \sum_{l=1}^{L} \tilde{\omega}_{l} \sum_{i=0}^{K} \frac{1}{\Sigma_{l_{i,i}}} (\theta_{l_i}^Y - f(dose_{i},\hat{\theta}_{m}))^2 + 2p

where p denotes the number of estimated parameters and K the number of active dose levels. Here as well for the simplified case the formula reduces to one summand as L=1. Corresponding gAIC based weights for model M are calculated as outlined in Schorning et al. (2016)

\Omega_I (M) = \frac{\exp(-0.5 gAIC_{M})}{\sum_{m=1}^{Q} \exp(-0.5 gAIC_{m})}

where Q denotes the number of models included in the averaging procedure.

Usage

getModelFits(models, dose_levels, posterior, avg_fit = TRUE, simple = FALSE)

Arguments

Value

An object of class modelFits. A list containing information about the fitted model coefficients, the prediction per dose group as well as maximum effect and generalized AIC (and corresponding weight) per model.

References

Schorning K, Bornkamp B, Bretz F, Dette H. 2016. Model selection versus model averaging in dose finding studies. Stat Med; 35; 4021-4040.

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("emax", "exponential", "sigEmax", "linear")
dose_levels    <- c(0, 1, 2, 4, 8)

fit        <- getModelFits(models      = models,
                           posterior   = posterior_list,
                           dose_levels = dose_levels)
                           
fit
                           
fit_simple <- getModelFits(models      = models,
                           posterior   = posterior_list,
                           dose_levels = dose_levels,
                           simple      = TRUE)
                           
fit_simple

getPosterior

Description

Either the patient level data or both mu_hat as well as S_hat must to be provided. If patient level data is provided mu_hat and S_hat are calculated within the function using a linear model. This function calculates the posterior distribution. Depending on the input for S_hat this step is either performed for every dose group independently via the RBesT function postmix() or the mvpostmix() function of the DoseFinding package is utilized. In the latter case conjugate posterior mixture of multivariate normals are calculated (DeGroot 1970, Bernardo and Smith 1994)

Usage

getPosterior(
  prior_list,
  data = NULL,
  mu_hat = NULL,
  S_hat = NULL,
  calc_ess = FALSE
)

Arguments

Details

Kindly note that one can sample from the posterior_list with lapply(posterior_list, RBesT::rmix, n = 10).

Value

posterior_list, a posterior list object is returned with information about (mixture) posterior distribution per dose group (more detailed information about the conjugate posterior in case of covariance input for S_hat is provided in the attributes)

References

BERNARDO, Jl. M., and Smith, AFM (1994). Bayesian Theory. 81.

Examples

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)

summary(posterior_list)

performBayesianMCP

Description

Performs Bayesian MCP Test step, as described in Fleischer et al. (2022). Tests for a dose-response effect using a model-based multiple contrast test based on the (provided) posterior distribution. In particular for every dose-response candidate the posterior probability is calculated that the contrast is bigger than 0 (based on the posterior distribution of the dose groups). In order to obtain significant test decision we consider the maximum of the posterior probabilities across the different models. This maximum is compared with a (multiplicity adjusted) critical value (on the probability scale).

Usage

performBayesianMCP(posterior_list, contr, crit_prob_adj)

Arguments

Value

Bayesian MCP test result, with information about p-values for the individual dose-response shapes and overall significance

References

Fleischer F, Bossert S, Deng Q, Loley C, Gierse J. 2022. Bayesian MCPMod. Pharmaceutical Statistics. 21(3): 654-670. doi:10.1002/pst.2193

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 = 0.05)
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)

performBayesianMCP(posterior_list = posterior_list,
                   contr          = contr_mat,
                   crit_prob_adj  = critVal)

performBayesianMCPMod

Description

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

Value

Bayesian MCP test result as well as modeling result.

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)

plot.modelFits

Description

Plot function based on the ggplot2 package. Providing visualizations for each model and a average Fit. Black lines show the fitted dose response models and an AIC based average model. Dots indicate the posterior median and vertical lines show corresponding credible intervals (i.e. the variability of the posterior distribution of the respective dose group). To assess the uncertainty of the model fit one can in addition visualize credible bands (default coloring as orange shaded areas). The calculation of these bands is performed via the getBootstrapQuantiles() function. The default setting is that these credible bands are not calculated.

Usage

## S3 method for class 'modelFits'
plot(
  x,
  gAIC = TRUE,
  cr_intv = TRUE,
  alpha_CrI = 0.05,
  cr_bands = FALSE,
  alpha_CrB = c(0.05, 0.2),
  n_bs_smpl = 1000,
  acc_color = "orange",
  plot_res = 100,
  ...
)

Arguments

Value

A ggplot2 object

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", "emax")
dose_levels <- c(0, 1, 2, 4, 8)
model_fits <- getModelFits(models      = models,
                           posterior   = posterior_list,
                           dose_levels = dose_levels,
                           simple       = TRUE)

plot(model_fits)

# plot with credible bands

plot(model_fits,
     cr_bands  = TRUE,
     n_bs_smpl = 1e2) 

predict.modelFits

Description

This function performs model predictions based on the provided model and dose specifications

Usage

## S3 method for class 'modelFits'
predict(object, doses = NULL, ...)

Arguments

Value

a list with the model predictions for the specified models and doses

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("emax", "exponential", "sigEmax", "linear")
dose_levels    <- c(0, 1, 2, 4, 8)
fit            <- getModelFits(models      = models,
                               posterior   = posterior_list,
                               dose_levels = dose_levels)

predict(fit, doses = c(0, 1, 3, 4, 6, 8))

simulateData

Description

Function to simulate patient level data for a normally distributed endpoint

Usage

simulateData(
  n_patients,
  dose_levels,
  sd,
  mods,
  n_sim = 1000,
  true_model = NULL,
  dr_means = NULL
)

Arguments

Value

A list object, containing patient level simulated data for all assumed true models. Also providing information about simulation iteration, patient number as well as dosage levels.

Examples

models <- DoseFinding::Mods(linear      = NULL,
                            linlog      = NULL,
                            emax        = c(0.5, 1.2),
                            exponential = 2, 
                            doses       = c(0, 0.5, 2,4, 8),
                            maxEff      = 6)
dose_levels <- c(0, 0.5, 2,4, 8)
sd          <- 12
n_patients  <- c(40, 60, 60, 60, 60)

sim_data <- simulateData(n_patients  = n_patients,
                         dose_levels = dose_levels,
                         sd          = sd,
                         mods        = models,
                         n_sim       = 100)

head(sim_data)