simulateData: simulateData

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

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

n_patients	Vector containing number of patients as a numerical value per dose-group.
dose_levels	Vector containing the different dosage levels.
sd	Standard deviation on patient level.
mods	An object of class "Mods" as specified in the DoseFinding package.
n_sim	Number of simulations to be performed, Default is 1000
true_model	Default value is NULL. Assumed true underlying model. Provided via a String. e.g. "emax". In case of NULL, all dose-response models, included in the mods input parameter will be used.
dr_means	a vector, with information about assumed effects per dose group. Default NULL.

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)

sim_data

BayesianMCPMod documentation built on May 29, 2024, 9:14 a.m.