Ways to Fit a PK Model with Some Data Below the Quantification Limit

By S. Beal. Journal of Pharmacokinetics and Pharmacodynamics, 2001

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Handling Data Below the Limit of Quantification in Mixed Effect Models (2009, methodology): “The influence of BQL samples and the performance of the likelihood based methods have previously only been assessed for the case where BQL samples occur in the terminal elimination phase of a one- or two-compartment PK model (5,7,8).”
Metrics for External Model Evaluation with an Application to the Population Pharmacokinetics of Gliclazide (2006, neutral): “measurements during the terminal phase [22].”
Likelihood based approaches to handling data below the quantification limit using NONMEM VI (2008, methodology): “The methods illustrated by Beal [4] can be described as follows: • Method 1 (M1): Discard BQL observations and apply extended least squares to the remaining observations.”
Population Pharmacokinetics of Colistin Methanesulfonate and Formed Colistin in Critically Ill Patients from a Multicenter Study Provide Dosing Suggestions for Various Categories of Patients (2011, methodology): “The equations below define the clearances due to CRRT. CLRRTCMS CLHDCMS FRRT (4) CLRRTC CLHDC FRRT (5) A thorough PK covariate analysis was performed for candidate covariates (both as fixed and random effects where appropriate).”