Methods to detect non-compliance and reduce its impact on population PK parameter estimates

  • Leonid Gibiansky
  • Ekaterina Gibiansky
  • Valerie Cosson
  • Nicolas Frey
  • Franziska Schaedeli Stark
Original Paper

Abstract

This work proposes and evaluates two methods (CM1 and CM2) for detecting non-compliance using concentration–time data and for obtaining estimates of population pharmacokinetic model parameters in a population with prevalent non-compliance. CM1 estimates individual residual variability (RV) and identifies subjects with higher than average RV as non-compliant. Exclusion of subjects with high RV from the analysis dataset reduces the bias in the estimates of the model parameters. Various methods of identification and exclusion of non-compliant subjects were tested, compared, and shown to reduce or eliminate bias in parameter estimates associated with non-compliance. The tested methods were (i) a pre-defined cutoff value of the random effect on RV, (ii) sequential exclusion of subjects with the highest RV percentiles, and (iii) use of a mixture model for RV. CM2 is applicable for the data with a specific sampling pattern that includes a potentially non-compliant outpatient part with several trough samples followed by a dense profile after the inpatient (compliant) dose. It relies only on the doses known to be administered (e.g., inpatient doses). In this method, all concentration measurements during the outpatient part of the study (except the trough value immediately preceding the inpatient dose) are removed from the dataset and an additional parameter (individual relative bioavailability of the outpatient doses) is introduced. For a number of simulated datasets with various sampling schemes and non-compliance patterns the proposed methods allowed to identify subjects with compliance problems and to reduce or eliminate bias in the estimates of the model parameters.

Keywords

Population PK Nonlinear mixed-effects modeling Patient compliance 

Supplementary material

10928_2014_9364_MOESM1_ESM.doc (263 kb)
Supplementary material 1 (DOC 263 kb)

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Leonid Gibiansky
    • 1
  • Ekaterina Gibiansky
    • 1
  • Valerie Cosson
    • 2
  • Nicolas Frey
    • 2
  • Franziska Schaedeli Stark
    • 2
  1. 1.QuantPharm LLCNorth PotomacUSA
  2. 2.Roche Innovation CenterBaselSwitzerland

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