is_ss: Determine steady state for pharmacokinetic observations

In nlmixr2autoinit: Automatic Generation of Initial Estimates for Population Pharmacokinetic Modeling

Determine steady state for pharmacokinetic observations

Description

Evaluates whether pharmacokinetic observations have reached steady state based on user-defined control settings. The classification can be based on a fixed number of doses, the number of half-lives relative to the dosing interval, or a combination of both criteria.

Usage

is_ss(df, ssctrl = ss_control(), half_life = NA)

Arguments

df	A data frame containing pharmacokinetic data. It should include columns for ID, EVID, SSflag, TIME, AMT, and tad.
ssctrl	A control list consistent with the structure returned by ss_control(). It specifies the method and thresholds for steady-state evaluation.
half_life	Numeric value representing the drug half-life. Required when the method in ss_control() is based on half-life or uses a combined approach.

Details

The function determines steady state by examining each observation in relation to prior dosing history. The required number of doses is calculated based on the specified method in ss_control(). Observation times are evaluated to confirm that dose interval and dose amount variability fall within acceptable limits and that the time after dose is within the most recent dosing interval. Observations manually marked as steady state using SSflag are also recognized as steady state.

Value

A data frame with added columns indicating steady-state status, the dosing interval for steady-state observations, and the method used to classify steady state.

See Also

ss_control()

Examples

dat <- pheno_sd
dat <- processData(dat)$dat
out <- is_ss(df = dat)
out[out$SteadyState == TRUE & !is.na(out$SteadyState),
    c("ID", "TIME", "DV", "EVID", "SteadyState")]

nlmixr2autoinit documentation built on Nov. 14, 2025, 1:07 a.m.