Compute the time to steady state using stepwise test of linear trend

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).

pk.tss.stepwise.linear(
  ...,
  min.points = 3,
  level = 0.95,
  verbose = FALSE,
  check = TRUE
)

Arguments

...

See pk.tss.data.prep()

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

See pk.tss.data.prep()

Value

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

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()