Empirical Likelihood Estimation for Population Pharmacokinetic Study Based on Generalized Linear Model

To obtain efficient estimation of parameters is a major objective in population pharmacokinetic study. In this paper, we propose an empirical likelihood-based method to analyze the population pharmacokinetic data based on the generalized linear model. A nonparametric version of the Wilk's theorem for the limiting distributions of the empirical likelihood ratio is derived. Simulations are conducted to demonstrate the accuracy and efficiency of empirical likelihood method. An application illustrating our methods and supporting the simulation study results is presented. The results suggest that the proposed method is feasible for population pharmacokinetic data.

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Dosing Recommendations for Vancomycin in Children and Adolescents with Varying Levels of Obesity and Renal Dysfunction: a Population Pharmacokinetic Study in 1892 Children Aged 1–18 Years

The AAPS Journal ◽

10.1208/s12248-021-00577-x ◽

2021 ◽

Vol 23 (3) ◽

Author(s):

Cornelis Smit ◽

Sebastiaan C. Goulooze ◽

Roger J. M. Brüggemann ◽

Catherine M. Sherwin ◽

Catherijne A. J. Knibbe

Keyword(s):

Renal Function ◽

Body Weight ◽

Children And Adolescents ◽

Pharmacokinetic Study ◽

Pediatric Population ◽

Pharmacokinetic Analysis ◽

Population Pharmacokinetic ◽

Pharmacokinetic Data ◽

Obese Children ◽

Population Pharmacokinetic Study

AbstractVancomycin is an effective but potentially nephrotoxic antibiotic commonly used for severe infections. Dosing guidelines for vancomycin in obese children and adolescents with or without renal impairment are currently lacking. This study describes the pharmacokinetics of vancomycin in a large pediatric cohort with varying degrees of obesity and renal function to design practical dosing guidelines for this population. A multi-center retrospective population pharmacokinetic study was conducted using data from patients aged 1−18 years who received >1 dose of vancomycin and had ≥1 vancomycin concentration measured between January 2006 and December 2012. Besides pharmacokinetic data, age, gender, body weight, creatinine clearance (CLcr, bedside Schwartz equation), ward, race, and neutropenic status were collected. Population pharmacokinetic analysis and simulations were performed using NONMEM7.4. A total of 1892 patients (5524 samples) were included, with total body weight (TBW) ranging 6−188 kg (1344 normal weight, 247 overweight, and 301 obese patients) and CLcr down to 8.6 mL/min/1.73 m2. The two-compartment model, with clearance (CL) significantly increasing with TBW and CLcr, central and peripheral volume of distribution and inter-compartmental clearance increasing with TBW, performed well for all age, weight, and renal function ranges. A dosing guideline is proposed that integrates body weight and CLcr resulting in effective and safe exposures across all ages, body weight, and renal functions in the pediatric population. We have characterized the full pharmacokinetic profile of vancomycin in obese children and adolescents aged 1−18 years and propose a practical dosing guideline that integrates both body weight and renal function.

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Generalized empirical likelihood methods for analyzing longitudinal data

Biometrika ◽

10.1093/biomet/asp073 ◽

2010 ◽

Vol 97 (1) ◽

pp. 79-93 ◽

Cited By ~ 28

Author(s):

Suojin Wang ◽

Lianfen Qian ◽

Raymond J. Carroll

Keyword(s):

Longitudinal Data ◽

Empirical Likelihood ◽

Simulation Study ◽

Normal Approximation ◽

Efficient Estimation ◽

Likelihood Method ◽

Finite Sample ◽

Estimation Of Parameters ◽

Generalized Empirical Likelihood ◽

Study Results

Abstract Efficient estimation of parameters is a major objective in analyzing longitudinal data. We propose two generalized empirical likelihood-based methods that take into consideration within-subject correlations. A nonparametric version of the Wilks theorem for the limiting distributions of the empirical likelihood ratios is derived. It is shown that one of the proposed methods is locally efficient among a class of within-subject variance-covariance matrices. A simulation study is conducted to investigate the finite sample properties of the proposed methods and compares them with the block empirical likelihood method by You et al. (2006) and the normal approximation with a correctly estimated variance-covariance. The results suggest that the proposed methods are generally more efficient than existing methods that ignore the correlation structure, and are better in coverage compared to the normal approximation with correctly specified within-subject correlation. An application illustrating our methods and supporting the simulation study results is presented.

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