What sampling strategies are used in population PK modeling?

• A. Rich sampling for individuals or sparse sampling across population; NLME uses both.
• B. Only rich sampling for individuals.
• C. Only sparse sampling across population.
• D. Random sampling.

Answer: (A)

Population PK modeling aims to describe what the drug does in the body for the typical individual and how people differ. To do this, sampling designs fall into two main approaches: richly detailed concentration-time data from a few subjects, and sparse samples from many subjects. Nonlinear mixed-effects modeling can handle either design and can even combine them in one analysis. Rich sampling gives precise, complete profiles that help pin down the structural model and parameter values for individuals. Sparse sampling across a population provides broad coverage, enabling robust estimation of the typical population parameters and the variability between subjects, by borrowing strength from the entire dataset. In practice, many studies use a mixed design—some subjects with dense sampling and others with sparse sampling—to maximize information while keeping study size and logistics reasonable. Random sampling alone doesn’t define the standard population PK approach; the key idea is using either rich data or sparse data across many subjects within a nonlinear mixed-effects framework.