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).
pk.tss.monoexponential(
...,
tss.fraction = 0.9,
output = c("population", "popind", "individual", "single"),
check = TRUE,
verbose = FALSE
)A scalar float for the first time when steady-state is achieved or
NA if it is not observed.
See pk.tss.data.prep()
The fraction of steady-state required for calling steady-state
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.
See pk.tss.data.prep().
Describe models as they are run, show convergence of the model (passed to the nlme function), and additional details while running.
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
Other Time to steady-state calculations:
pk.tss(),
pk.tss.stepwise.linear()