Create model — create_model • pharmr.extra

This is essentially a wrapper around the model-creation and -modification functionality in pharmr/Pharmpy.

Usage

create_model(
  route = c("auto", "oral", "sc", "im", "extravascular", "iv"),
  lag_time = FALSE,
  n_transit_compartments = 0,
  bioavailability = FALSE,
  n_cmt = 1,
  elimination = c("linear", "michaelis-menten"),
  iiv = "all",
  iiv_type = "exp",
  tmdd_type = NULL,
  tmdd_dv_types = list(drug = 1, target = 2),
  metabolite = FALSE,
  ruv = c("additive", "proportional", "combined", "ltbs"),
  force_ode = FALSE,
  covariates = NULL,
  scale_observations = NULL,
  data = NULL,
  name = NULL,
  estimation_method = "foce",
  estimation_options = list(),
  uncertainty_method = c("sandwich", "smat", "rmat", "efim", "none"),
  sir_options = list(niter = -1),
  blq_method = NULL,
  lloq = NULL,
  tool = c("nonmem", "nlmixr", "nlmixr2"),
  tables = c("fit"),
  full_tables = FALSE,
  auto_init = TRUE,
  auto_stack_encounters = TRUE,
  mu_reference = "auto",
  settings = list(),
  verbose = FALSE
)

Arguments

route: route of administration, either oral or iv
lag_time: add a lag time, default is FALSE
n_transit_compartments: number of transit-compartments for absorption model. Default is 0.
bioavailability: Add a bioavailability parameter? Default is FALSE. Will add using a logit function.
n_cmt: number of elimination and distribution compartments. Default is 1, i.e. no peripheral distributions.
elimination: elimination type, either linear or michaelis-menten.
iiv: either character or a list object. If character, should be either "basic" (only CL and V parameters) or "all" (IIV on all parameters). If specified as a list object, it should contain the IIV magnitude (on SD scale) for parameters and potential correlations specified using a tilde, e.g. list("CL" = 0.2, "V" = 0.3, "CL~V" = 0.1).
iiv_type: either character or list. If character, one of c("exp", "add", "prop", "log", "re_log"). If list, should specify for each parameter the effect type, e.g. list(CL = "add", V = "exp"). Default is "exp" for all.
tmdd_type: if a TMDD model structure is desired, can choose one of full, ib, crib, cr, qss, or mmapp. See Pharmpy/pharmr documentation for details.
metabolite: either TRUE/FALSE or a list. If logical, determines if a metabolite compartment be added (with input from central using linear kinetics). In this case, DVID in the dataset should be set to 1 for the parent compound and 2 for the metabolite. If argument is a list object, it will require list elements dvid_drug (integer) and presystemic (logical) to be specified. dvid_drug specified the index number for parent drug in the DVID column in the dataset. presystemic determines whether the metabolite is formed before absorption (TRUE), or after systemic absorption (FALSE).
ruv: one of proportional, additive, or combined.
force_ode: force creation of a model with ODEs, even though the model is linear. Can be FALSE (default), TRUE, or ADVAN number (for NONMEM models). In the latter case, options are either 6, 9, or 13.
covariates: list of parameter-covariate effects, e.g. list(CL = list(WT = "pow", CRCL = "lin"), V = list(WT = "pow") Values in list need to match one of the effects allowed by pharmpy.
scale_observations: scale observations by factor, e.g. due to unit differences between dose and concentration. E.g. scale_observations = 1000 will add S1 = V/1000 (for a 1-compartment model) to NONMEM code.
data: data.frame as input to NONMEM / nlmixr.
name: name of model
estimation_method: character vector of one or more estimation methods. A single method (e.g. "foce") sets one estimation step; a vector (e.g. c("saem", "imp")) creates sequential estimation steps. Available methods: "fo", "foce", "its", "impmap", "imp", "saem".
estimation_options: options for estimation method(s). For a single estimation method, specify a flat list, e.g. list(NITER = 50). For multiple estimation methods, specify a named list of lists where names match the method names, e.g. list(SAEM = list(NBURN = 500), IMP = list(NITER = 10)). Methods not listed will use their default options. If a flat list is provided with multiple estimation methods, it applies to the first step only.
uncertainty_method: Compute uncertainty for parameter estimations. One of sandwich (default), smat, fmat, efim, or none.
sir_options: options for running SIR in covariance step. A list with options niter and samples. If niter <= 0 (default), SIR will not be run.
blq_method: method for handling data below the limit of quantification. Available options are m1, m3, m4, m5, m6, m7, as described by Beal et al. Default is no handling of BLQ data (NULL).
lloq: (optional) a numeric value specifying the limit of quantification for observations. Will be disregarded if an LLOQ column is in the dataset.
tool: output model type, either nonmem or nlmixr
tables: which pre-specified tables to add, defaults to parameters and fit tables.
full_tables: For the default tables, should all input columns from be included in the output tables? Default FALSE.
auto_init: automatically update initial estimates to reasonable values based on a crude assessment of the PK data. Default is TRUE.
auto_stack_encounters: detects if TIME within an individual is decreasing from one record to another, which NONMEM cannot handle. If this happens, it will add a reset event (EVID=3) at that time, and increase the TIME for subsequent events so that NONMEM does not throw an error. It will increase the time for the next encounter to the maximum encounter length across all subjects in the dataset (rounded up to 100). If no decreasing TIME is detected, nothing will be done (most common case). This feature is useful e.g. for crossover trials when data on the same individual ispresent but is included in the dataset as time-after-dose and not actual time since first overall dose.
mu_reference: Control mu-referencing of the model. "auto" (default) applies mu-referencing automatically when estimation_method = "saem". TRUE always applies mu-referencing. FALSE never applies it.
settings: additional settings for model creation and model estimation. TBD
verbose: verbose output?

Value

TODO