R: Compute the time to steady state using nonlinear,...

pk.tss.monoexponential {PKNCA}

R Documentation

Compute the time to steady state using nonlinear, mixed-effects modeling of trough concentrations.

Description

Trough concentrations are selected as concentrations at the time of dosing. An exponential curve is then fit through the data with a different magnitude by treatment (as a factor) and a random steady-state concentration and time to stead-state by subject (see random.effects argument).

Usage

pk.tss.monoexponential(
  ...,
  tss.fraction = 0.9,
  output = c("population", "popind", "individual", "single"),
  check = TRUE,
  verbose = FALSE
)

Arguments

`...`	See `pk.tss.data.prep()`
`tss.fraction`	The fraction of steady-state required for calling steady-state
`output`	Which types of outputs should be produced? `population` is the population estimate for time to steady-state (from an nlme model), `popind` is the individual estimate (from an nlme model), `individual` fits each individual separately with a gnls model (requires more than one individual; use `single` for one individual), and `single` fits all the data to a single gnls model.
`check`	See `pk.tss.data.prep()`.
`verbose`	Describe models as they are run, show convergence of the model (passed to the nlme function), and additional details while running.

Value

A scalar float for the first time when steady-state is achieved or NA if it is not observed.

References