tdmore: Append TDM functionality to a pharmacometrics structural...

In tdmore-dev/tdmore: In silico evaluation of precision dosing

Description

Append TDM functionality to a pharmacometrics structural model.

Usage

## S3 method for class 'RxODE'
tdmore(model, res_var, parameters = NULL, omega = NULL, iov = NULL, ...)

tdmore(model, ...)

## S3 method for class 'nlmixrUI'
tdmore(model, iov = NULL, ...)

## S3 method for class 'nlmixrFitCore'
tdmore(model, ...)

Arguments

model	the base model
res_var	the residual variability
parameters	list of parameter names, or NULL to automatically detect The automatic detection will analyze omega first, to see if there are names present. If not, it will use all parameters from the RxODE model
omega	omega variance-covariance matrix, or NULL to use a diagonal matrix of variance 1 for all input parameters
iov	list of parameter names related to IOV, NULL if no IOV
...	extra arguments will be passed to the underlying structural model

Value

An object of class tdmore, which can be used to estimate posthoc bayesian parameters

Note

You can use a named omega parameter to distinguish between unexplained variability (described by an a priori distribution) and a covariate (assumed to be known for all individuals). Any input parameters not provided in omega are assumed to be covariates.

Examples

modelCode <- "
CL = 23.6 * exp(ETA1*0.42);
Vc = 1070 * exp(ETA2*1.11);
ka=4.48;
CONC = centr / Vc * 1000;
d/dt(abs) = -ka*abs;
d/dt(centr) = ka*abs - CL/Vc*centr;
"
rxodeModel <- RxODE::RxODE(modelCode)
model <- tdmore(rxodeModel, res_var=list(errorModel(var="CONC", add=3.7)))

tdmore-dev/tdmore documentation built on Jan. 1, 2022, 3:21 a.m.