title: An alternative method for population pharmacokinetic data analysis under noncompliance.
abstract: Noncompliance presents a persistent problem while analyzing PK data from outpatient clinical studies. Ignoring dose omission or making uninformed assumptions about patient drug intake history can prove detrimental to the objectives of the analysis (e.g. determining the PK model parameters or identifying covariates) and ultimately compromise the interpretation of the data. In order to overcome this problem, an alternative method of handling noncompliant data is evaluated in this report. The proposed approach is based on the principle of superposition and works by separating the estimation of the elimination rate from the model based steady-state PK concentration. Simulations implementing this method under different scenarios of noncompliance demonstrate that it performs better than the conventional method of analyzing population PK data when compared on the basis of bias and imprecision in parameter estimation and power (and type I error) for covariate detection. Overall, the new method exhibits great potential to address the issue of uncertain/unreliable dosing histories frequently encountered in clinical trials.
authors.affiliation: Clinical Pharmacology (Pharmacometrics), Pfizer Global Research and Development, 2800 Plymouth Road, Ann Arbor, MI 48105, USA. pankaj.gupta@pfizer.com
authors.forename: Pankaj
authors.initials: P
authors.lastname: Gupta
authors.forename: Matthew M
authors.initials: MM
authors.lastname: Hutmacher
authors.forename: Bill
authors.initials: B
authors.lastname: Frame
authors.forename: Raymond
authors.initials: R
authors.lastname: Miller
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D004305:Dose-Response Relationship, Drug; D006801:Humans; D007408:Intestinal Absorption; D015233:Models, Statistical; D010599:Pharmacokinetics; D011153:Population; D016312:Treatment Refusal / Q000706:statistics & numerical data*