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Methods to detect non-compliance and reduce its impact on population PK parameter estimates
Leonid Gibiansky1, Ekaterina Gibiansky, Valerie Cosson
1QuantPharm LLC, North Potomac, MD, USA, lgibiansky@quantpharm.com.
Journal of Pharmacokinetics and Pharmacodynamics
|June 22, 2014
Summary
Two new methods (CM1 and CM2) effectively detect patient non-compliance using concentration-time data. These approaches improve pharmacokinetic model parameter estimation by identifying and excluding non-compliant subjects, reducing bias in population analyses.
Area of Science:
- Pharmacometrics
- Pharmacokinetics
- Statistical Modeling
Background:
- Non-compliance in patients significantly biases pharmacokinetic (PK) model parameter estimates.
- Accurate PK parameter estimation is crucial for optimizing drug therapy and understanding drug behavior in populations.
Purpose of the Study:
- To propose and evaluate two novel methods (CM1 and CM2) for detecting non-compliance using concentration-time data.
- To improve the accuracy of population pharmacokinetic model parameter estimates in the presence of non-compliance.
Main Methods:
- CM1: Estimates individual residual variability (RV) to identify non-compliant subjects for exclusion, utilizing cutoff values, percentile exclusion, or mixture models.
- CM2: Applicable for specific sampling schemes, it removes outpatient data and introduces a relative bioavailability parameter to account for non-compliance.
Main Results:
- Both CM1 and CM2 successfully identified subjects with compliance issues in simulated datasets.
- Exclusion of non-compliant subjects using the proposed methods significantly reduced or eliminated bias in PK model parameter estimates.
Conclusions:
- The developed methods (CM1 and CM2) offer robust strategies for handling non-compliance in pharmacokinetic studies.
- These approaches enhance the reliability of population PK models by mitigating bias introduced by patient non-adherence.
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