Compute the time to steady state using stepwise test of linear trend
Source:R/tss.stepwise.linear.R
pk.tss.stepwise.linear.Rd
A linear slope is fit through the data to find when it becomes
non-significant. Note that this is less preferred than the
pk.tss.monoexponential
due to the fact that with more time or more subjects
the performance of the test changes (see reference).
Arguments
- ...
- min.points
The minimum number of points required for the fit
- level
The confidence level required for assessment of steady-state
- verbose
Describe models as they are run, show convergence of the model (passed to the nlme function), and additional details while running.
- check
Details
The model is fit with a different magnitude by treatment (as a factor, if
given) and a random slope by subject (if given). A minimum of min.points
is required to fit the model.
References
Maganti L, Panebianco DL, Maes AL. Evaluation of Methods for Estimating Time to Steady State with Examples from Phase 1 Studies. AAPS Journal 10(1):141-7. doi:10.1208/s12248-008-9014-y
See also
Other Time to steady-state calculations:
pk.tss()
,
pk.tss.monoexponential()