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simulateData

Source: R/simulation.R
simulateData.Rd

Function to simulate patient level data for a normally distributed endpoint

Usage

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

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. Can be NULL if probability_scale is TRUE. Default NULL.

mods

An object of class "Mods" as specified in the DoseFinding package. Can be NULL if ´dr_means´ is not NULL. Default NULL.

n_sim

Number of simulations to be performed, Default is 1000

true_model

A character for model name, e.g. "emax". Assumed true underlying model. If NULL, all dose-response models included in the mods input parameter will be used. Default NULL.

dr_means

an optional vector, with information about assumed effects per dose group. Default NULL.

probability_scale

A boolean to specify if the trial has a continuous or a binary outcome. Setting to TRUE will transform calculations from the logit scale to the probability scale, which can be desirable for a binary outcome. Default FALSE.

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)

head(sim_data)
#>   simulation ptno dose      linear      linlog       emax1       emax2
#> 1          1    1    0 -13.2621761 -13.2621761 -13.2621761 -13.2621761
#> 2          1    2    0  -0.5481451  -0.5481451  -0.5481451  -0.5481451
#> 3          1    3    0  -2.4842980  -2.4842980  -2.4842980  -2.4842980
#> 4          1    4    0  -7.7072241  -7.7072241  -7.7072241  -7.7072241
#> 5          1    5    0 -10.1674908 -10.1674908 -10.1674908 -10.1674908
#> 6          1    6    0  -8.9146060  -8.9146060  -8.9146060  -8.9146060
#>   exponential
#> 1 -13.2621761
#> 2  -0.5481451
#> 3  -2.4842980
#> 4  -7.7072241
#> 5 -10.1674908
#> 6  -8.9146060

# custom response "model" shape
custom_dose_response <- c(1, 2, 3, 4, 5)
sim_data_custom_dr   <- simulateData(n_patients  = n_patients,
                                     dose_levels = dose_levels,
                                     sd          = sd,
                                     dr_means    = custom_dose_response)

head(sim_data_custom_dr)
#>   simulation ptno dose  dr_response
#> 1          1    1    0  -8.34065020
#> 2          1    2    0  -0.03767594
#> 3          1    3    0   7.31990133
#> 4          1    4    0 -10.88546421
#> 5          1    5    0  21.76409509
#> 6          1    6    0   8.14677622