Create a data object for use in get_mcmc_posterior()

Usage

new_stan_data(
  regimen,
  covariates,
  data,
  parameters,
  fixed = NULL,
  iiv,
  ruv,
  dose_cmt = 1,
  ltbs = FALSE,
  verbose = FALSE
)

Arguments

regimen: Regimen object (created by PKPDsim::new_regimen())
covariates: List of covariate objects created by PKPDsim::new_covariate()
data: Data frame with columns t, dv, and cmt
parameters: list of population parameters, e.g. list(CL = 5, V = 50)
fixed: optional, vector of parameters to fix and not sample.
iiv: list of inter-individual variability for parameters. Should have exact same list elements as parameters, and magnitude supplied on SD scale.
ruv: magnitude of residual unexplained variability (RUV). Should be a list specifying proportional and/or additive error magnitude on standard deviation scale, e.g. list("prop" = 0.1, "add" = 1). If ltbs is TRUE, should specify only an add part, which applies an additive error on the log-scale (which then becomes an approximate proportional error).
dose_cmt: Specify what dose compartment. Observation compartment in dataset is irrelevant, handled in model.
ltbs: use log-transform-both-sides approach for observations? Default is FALSE.
verbose: verbosity

Value

Named list suitable for passing on to Torsten.

Examples

regimen <- PKPDsim::new_regimen(
  amt = 1500, 
  n = 4, 
  times = c(0, 12, 24, 36), 
  type = 'infusion'
)
covariates <- list(
  WT = PKPDsim::new_covariate(
    value = c(150, 149.5),
    times = c(0, 30),
    unit = "kg"
  ),
  CRCL = PKPDsim::new_covariate(
    value = c(6.5, 6.7),
    times = c(0, 12),
    unit = "l/hr"
  )
)
tdm_data <- data.frame(
  t = c(1, 2), 
  dv = c(900, 800), 
  cmt = c(2, 2)
)
new_stan_data(
  regimen, 
  covariates, 
  tdm_data,
  parameters = list(CL = 5, V = 50),
  iiv = list(CL = 0.1, V = 0.2),
  ruv = list(prop = 0.1, add = 1)
)
#> $parameters
#> $parameters$CL
#> [1] 5
#> 
#> $parameters$V
#> [1] 50
#> 
#> 
#> $fixed
#> NULL
#> 
#> $regimen
#>    t     type  amt t_inf
#> 1  0 infusion 1500     1
#> 2 12 infusion 1500     1
#> 3 24 infusion 1500     1
#> 4 36 infusion 1500     1
#> 
#> $covariates
#> $covariates$WT
#>   value times unit implementation
#> 1 150.0     0   kg    interpolate
#> 2 149.5    30   kg    interpolate
#> 
#> $covariates$CRCL
#>   value times unit implementation
#> 1   6.5     0 l/hr    interpolate
#> 2   6.7    12 l/hr    interpolate
#> 
#> 
#> $data
#>   t  dv cmt
#> 1 1 900   2
#> 2 2 800   2
#> 
#> $iiv
#> $iiv$CL
#> [1] 0.1
#> 
#> $iiv$V
#> [1] 0.2
#> 
#> 
#> $ruv
#> $ruv$prop
#> [1] 0.1
#> 
#> $ruv$add
#> [1] 1
#> 
#> 
#> $stan_data
#> $stan_data$ID
#> [1] 1 1 1 1 1 1 1 1 1
#> 
#> $stan_data$time
#> [1]  0  0  1  2 12 12 24 30 36
#> 
#> $stan_data$cmt
#> [1] 1 2 2 2 1 2 1 2 1
#> 
#> $stan_data$DV
#> [1]   0   0 900 800   0   0   0   0   0
#> 
#> $stan_data$amt
#> [1] 1500    0    0    0 1500    0 1500    0 1500
#> 
#> $stan_data$evid
#> [1] 1 2 0 0 1 2 1 2 1
#> 
#> $stan_data$MDV
#> [1] 1 0 0 0 1 0 1 0 1
#> 
#> $stan_data$rate
#> [1] 1500    0    0    0 1500    0 1500    0 1500
#> 
#> $stan_data$WT
#> [1] 150.0 150.0 150.0 150.0 150.0 150.0 150.0 149.5 149.5
#> 
#> $stan_data$CRCL
#> [1] 6.5 6.5 6.5 6.5 6.5 6.7 6.7 6.7 6.7
#> 
#> $stan_data$addl
#> [1] 0 0 0 0 0 0 0 0 0
#> 
#> $stan_data$ss
#> [1] 0 0 0 0 0 0 0 0 0
#> 
#> $stan_data$ii
#> [1] 0 0 0 0 0 0 0 0 0
#> 
#> $stan_data$theta_CL
#> [1] 5
#> 
#> $stan_data$omega_CL
#> [1] 0.1
#> 
#> $stan_data$theta_V
#> [1] 50
#> 
#> $stan_data$omega_V
#> [1] 0.2
#> 
#> $stan_data$dv_pk
#> [1] 900 800
#> 
#> $stan_data$i_obs_pk
#> [1] 3 4
#> 
#> $stan_data$n_obs_pk
#> [1] 2
#> 
#> $stan_data$ruv_prop_pk
#> [1] 0.1
#> 
#> $stan_data$ruv_add_pk
#> [1] 1
#> 
#> $stan_data$ltbs_pk
#> [1] FALSE
#> 
#> $stan_data$n_t
#> [1] 9
#> 
#>