Compute the time to steady state using nonlinear, mixed-effects modeling of trough concentrations.
Source:R/tss.monoexponential.R
pk.tss.monoexponential.Rd
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
- ...
- 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; usesingle
for one individual), andsingle
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.
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()