Evaluation of hypothesis testing for comparing two populations using NONMEM analysis

White, Donald B.; Walawander, Cynthia A.; Liu, Dong Y.; Grasela, Thaddeus H.

doi:10.1007/bf01062529

Cited by 41 publications

(23 citation statements)

References 9 publications

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“…At each step, a specific assumption was tested (e.g., one-compartment versus two-compartment model). The main criterion of decision was the likelihood ratio test (25). The level of significance was 0.05.…”

Section: Methodsmentioning

confidence: 99%

Pharmacokinetics of Oral Acyclovir in Neonates and in Infants: a Population Analysis

Tod

Lokiec

Bidault

et al. 2001

Antimicrob Agents Chemother

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Acyclovir is approved for the treatment of herpes simplex virus (HSV) and varicella-zoster virus (VZV)infections in children by the intravenous and oral routes. However, its use by the oral route in children younger than 2 years of age is limited due to a lack of pharmacokinetic data. The objectives of the present study were to determine the typical pharmacokinetics of an oral suspension of acyclovir given to children younger than 2 years of age and the interindividual variabilities in the values of the pharmacokinetic parameters in order to support the proposed dosing regimen (24 mg/kg of body weight three times a day for patients younger than 1 month of age or four times a day otherwise). Children younger than age 2 years with HSV or VZV infections were enrolled in a multicenter study. Children were treated for at least 5 days with an acyclovir oral suspension. Plasma samples were obtained at steady state, before acyclovir administration, and at 2, 3, 5, and 8 h after acyclovir administration. Acyclovir concentrations were measured by radioimmunoassay. The data were analyzed by a population approach. Data for 79 children were considered in the pharmacokinetic study (212 samples, 1 to 5 samples per patient). Acyclovir clearance was related to the estimated glomerular filtration rate, body surface area, and serum creatinine level. The volume of distribution was related to body weight. The elimination half-life decreased sharply during the first month after birth, from 10 to 15 h to 2.5 h. Bioavailability was 0.12. The interindividual variability was less pronounced when the parameters were normalized with respect to body weight. Hence, dosage adjustment by body weight is recommended for this population. Simulations showed that the length of time that acyclovir remains above the 50% inhibitory concentration during a 24-h period was more than 12 h for HSV but not for VZV. The proposed dosing regimen seems adequate for the treatment of HSV infections, while for the treatment of VZV infections, a twofold increase in the dose seems necessary for children older than age 3 months.Acyclovir is currently used for the prevention and treatment of herpes simplex virus (HSV) and varicella-zoster virus (VZV) infections (7). It is available at different dosages in the form of tablets, oral suspensions (containing 200, 400, or 800 mg in 10 ml), and injectable solutions. About 20 clinical studies have documented the use of acyclovir in children (for a review, see reference 24). Most frequently, acyclovir has been administered intravenously. Hintz et al. (8) recommended 10 mg/kg of body weight every 8 h (q8h) for neonates, while Blum et al.(2) recommended 250 mg/m 2 (for HSV infections) and 500 mg/m 2 (for HSV encephalitis and VZV infections) q8h in children between 3 months and 12 years of age. Owing to the ease of its administration and dosage adjustment, the oral suspension is also used in children. The recommended dosage in neonates is 100 mg four times a day (q.i.d.) (HSV infections) and 200 mg q.i.d. (for VZV infections). I...

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Section: Methodsmentioning

confidence: 99%

Pharmacokinetics of Oral Acyclovir in Neonates and in Infants: a Population Analysis

Tod

Lokiec

Bidault

et al. 2001

Antimicrob Agents Chemother

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“…For such population pharmacokinetic experiments, there are simulation studies in the literature designed to assess the effect of the sample size of the study on the power of the design to test specific hypothesis. These are based on the likelihood ratio test [32,33], t-test [34] or confidence interval [33] approach for determining the sample size required to detect the difference in parameter values between subpopulations. Also analytical methods based on the Wald chi-squared test have been described for calculating sample size for detecting differences between subpopulations [35][36][37].…”

Section: Sample Size Calculation For Population Pharmacokinetic Expermentioning

confidence: 99%

Optimal Design of Pharmacokinetic Studies

Aarons

Ogungbenro

2010

Basic Clin Pharma Tox

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Experimental design is fundamental to successful scientific investigation. Poorly designed experiments lead to the loss of information, which is costly and potentially unethical. Experiments can be designed in an optimal fashion to maximize the amount of information they provide. Optimal design theory uses prior information about the model and parameter estimates to optimize a function of the Fisher information matrix to obtain the best combination of the design factors. In the case of population pharmacokinetic experiments, this involves the selection and a careful balance of a number of design factors, including the number and location of measurement times and the number of subjects to include in the study. It is expected that as the awareness about the benefits of this approach increases, more people will embrace it and ultimately will lead to more efficient population pharmacokinetic experiments and can also help to reduce both cost and time during drug development. This MiniReview provides an introduction to optimal design using examples taken from different pharmacokinetic experiments.There is a long tradition of statisticians helping research workers in the design and analysis of their experiments [1][2][3][4]. The early work was directed towards linear models with analysis being performed by analysis of variance. Beginning in the 1970s, the work was extended to the consideration of nonlinear (statistical) models [5-9] with seminal work being contributed by Fedorov [10,11]. Somewhat later, these considerations were applied to pharmacokinetic experiments [12][13][14][15]. More recently, this work has been extended to population pharmacokinetic studies which involves the use of nonlinear mixed effects models [16][17][18][19][20][21][22].The design of an experiment involves the selection of the conditions under which the experiment is performed. In a pharmacokinetic experiment, this will mean the selection of the doses employed, the number and type of subject, the number of blood samples taken per subject and the times when those samples are taken. An optimal design is one in which the design variables are chosen so as to maximize the information that can be obtained from an experiment. The efficient design of an experiment leads to a reduction in costs, which also has an ethical dimension if fewer subjects are required.In the next section, we will consider some simple models to illustrate the concepts of optimal design. This will be followed by a consideration of population pharmacokinetics in which we will discuss the optimization of sampling times, sampling windows and we will describe methods for the calculation of the power of a study. In the following section, we will illustrate the approach using a drug interaction example. Finally, we will describe extensions of the work to models involving discrete data rather than continuous data and briefly discuss software issues. Simple ExamplesThe simplest regression model that we can consider is the straight line

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“…The Bayesian estimation method Clinical Chemistry 49, No. 7,2003 has been implemented in several commercially available pharmacokinetic programs (e.g., NONMEM, PKS, USC-PACK, P-PHARM, and MWPharm).…”

Section: Discussionmentioning

confidence: 99%

“…At each step, a specific assumption was tested (e.g., one-compartment vs two-compartment model or correlation between clearance and body surface area). The main criterion of decision was the likelihood ratio test (7 ). For hierarchical models, the difference between their respective objective function values is approximately 2 -distributed, and as a result, formal testing can be performed.…”

Section: Population Pharmacokinetic Analysismentioning

confidence: 99%

Optimal Sampling Strategies to Assess Inulin Clearance in Children by the Inulin Single-Injection Method

et al. 2003

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Evaluation of hypothesis testing for comparing two populations using NONMEM analysis

Cited by 41 publications

References 9 publications

Pharmacokinetics of Oral Acyclovir in Neonates and in Infants: a Population Analysis

Pharmacokinetics of Oral Acyclovir in Neonates and in Infants: a Population Analysis

Optimal Design of Pharmacokinetic Studies

Optimal Sampling Strategies to Assess Inulin Clearance in Children by the Inulin Single-Injection Method

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