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

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

Maganti, L., Panebianco, D.L. & Maes, A.L. Evaluation of Methods for Estimating Time to Steady State with Examples from Phase 1 Studies. AAPS J 10, 141–147 (2008). https://doi.org/10.1208/s12248-008-9014-y

See also

Other Time to steady-state calculations: pk.tss(), pk.tss.stepwise.linear()