Modeling and Simulation of Count Data

Plan, Elodie L.

doi:10.1038/psp.2014.27

Cited by 52 publications

(40 citation statements)

References 59 publications

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“…Daily vomit frequencies over 8 days were evaluated using a Poisson model, as previously described (19,20). The suitability of the Poisson model was evaluated by plotting the individual mean frequency against variance to evaluate for equidispersion.…”

Section: Genotypingmentioning

confidence: 99%

N -Acetyltransferase Genotypes and the Pharmacokinetics and Tolerability of para -Aminosalicylic Acid in Patients with Drug-Resistant Pulmonary Tuberculosis

Kock

Diacon

et al. 2015

Antimicrob Agents Chemother

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h The aim of this study was to examine the relationships between N-acetyltransferase genotypes, pharmacokinetics, and tolerability of granular slow-release para-aminosalicylic acid (GSR-PAS) in tuberculosis patients. The study was a randomized, two-period, open-label, crossover design wherein each patient received 4 g GSR-PAS twice daily or 8 g once daily alternately. The PAS concentration-time profiles were modeled by a one-compartment disposition model with three transit compartments in series to describe its absorption. Patients' NAT1 and NAT2 genotypes were determined by sequencing and restriction enzyme analysis, respectively. The number of daily vomits was modeled by a Poisson probability mass function. Comparisons of other tolerability measures by regimens, gender, and genotypes were evaluated by a linear mixed-effects model. The covariate effects associated with efavirenz, gender, and NAT1*3, NAT1*14, and NAT2*5 alleles corresponded to 25, 37, ؊17, ؊48, and ؊27% changes, respectively, in oral clearance of PAS. The NAT1*10 allele did not influence drug clearance. The time above the MIC of 1 mg/liter was significantly different between the two regimens but not influenced by the NAT1 or NAT2 genotypes. The occurrence and intensity of intolerance differed little between regimens. Four grams of GSR-PAS twice daily but not 8 g once daily ensured concentrations exceeding the MIC (1 mg/liter) throughout the dosing interval; PAS intolerance was not related to maximum PAS concentrations over the doses studied and was not more frequent after once-daily dosing. We confirm that the slow phenotype conferred by the NAT1*14 and NAT1*3 alleles resulted in higher PAS exposure but found no evidence of increased activity of the NAT1*10 allele. p ara-Aminosalicylic acid (PAS) was the first effective antituberculosis agent used to treat pulmonary tuberculosis (1); for a duration of 20 to 25 years, it was part of the standard "first-line" tuberculosis treatment (2). Valued for preventing resistance in companion drugs, it was nonetheless notorious for gastrointestinal intolerance, causing frequent nausea, vomiting, and abdominal discomfort. The replacement of PAS with rifampin and ethambutol was greeted with relief by patients, but with widespread multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis in a number of countries, particularly in the developing world, PAS is again being used to treat drug-resistant tuberculosis.The PAS preparation most commonly used in many countries is the granular slow-release (delayed-release) formulation (GSR-PAS), which prevents premature drug release in the stomach, avoiding the high PAS concentrations considered prone to cause intolerance. Several studies (3-7) have reported PAS concentrations associated with use of GSR-PAS in dosages from 4 g daily to 8 to 12 g daily in divided doses, as recommended by the World Health Organization (8). As PAS is usually considered bacteriostatic, divided dosing aims to provide concentrations consistently exceeding the PAS MIC of approx...

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Section: Genotypingmentioning

confidence: 99%

N -Acetyltransferase Genotypes and the Pharmacokinetics and Tolerability of para -Aminosalicylic Acid in Patients with Drug-Resistant Pulmonary Tuberculosis

Kock

Diacon

et al. 2015

Antimicrob Agents Chemother

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“…Likelihood ratio test is applied to investigate model improvement between hierarchical models to describe count data using Poisson model [12]. Simulation-based approach is the most common method to evaluate ability of Poisson model to describe the variability and structure of count data and these approaches, such as VPC, were conducted for these two real cases.…”

Section: Discussionmentioning

confidence: 99%

Approaches for modeling within subject variability in pharmacometric count data analysis: dynamic inter-occasion variability and stochastic differential equations

Deng

Plan

Karlsson

2016

J Pharmacokinet Pharmacodyn

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Parameter variation in pharmacometric analysis studies can be characterized as within subject parameter variability (WSV) in pharmacometric models. WSV has previously been successfully modeled using inter-occasion variability (IOV), but also stochastic differential equations (SDEs). In this study, two approaches, dynamic inter-occasion variability (dIOV) and adapted stochastic differential equations, were proposed to investigate WSV in pharmacometric count data analysis. These approaches were applied to published count models for seizure counts and Likert pain scores. Both approaches improved the model fits significantly. In addition, stochastic simulation and estimation were used to explore further the capability of the two approaches to diagnose and improve models where existing WSV is not recognized. The results of simulations confirmed the gain in introducing WSV as dIOV and SDEs when parameters vary randomly over time. Further, the approaches were also informative as diagnostics of model misspecification, when parameters changed systematically over time but this was not recognized in the structural model. The proposed approaches in this study offer strategies to characterize WSV and are not restricted to count data.

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“…Count data can be encountered in both preclinical and clinical pharmacodynamic (PD) studies . It consists of non‐negative integer values that record the number of discrete occurrences often linked to explanatory variables.…”

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confidence: 99%

“…However, when the data exhibit bimodal peaks, incorporation of drug effect is not so straightforward because there may not exist a unique parameter characterizing the distribution. Furthermore, although some diagnostic tools have been developed for the count model, most of them are built for unimodal data. The evaluation of the bimodal model is therefore a challenge.…”

mentioning

confidence: 99%

New generalized poisson mixture model for bimodal count data with drug effect: An application to rodent brief‐access taste aversion experiments

Sheng

Soto

Gül

et al. 2016

CPT Pharmacom & Syst Pharma

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Pharmacodynamic (PD) count data can exhibit bimodality and nonequidispersion complicating the inclusion of drug effect. The purpose of this study was to explore four different mixture distribution models for bimodal count data by including both drug effect and distribution truncation. An example dataset, which exhibited bimodal pattern, was from rodent brief‐access taste aversion (BATA) experiments to assess the bitterness of ascending concentrations of an aversive tasting drug. The two generalized Poisson mixture models performed the best and was flexible to explain both under and overdispersion. A sigmoid maximum effect (Emax) model with logistic transformation was introduced to link the drug effect to the data partition within each distribution. Predicted density‐histogram plot is suggested as a model evaluation tool due to its capability to directly compare the model predicted density with the histogram from raw data. The modeling approach presented here could form a useful strategy for modeling similar count data types.

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Modeling and Simulation of Count Data

Cited by 52 publications

References 59 publications

N -Acetyltransferase Genotypes and the Pharmacokinetics and Tolerability of para -Aminosalicylic Acid in Patients with Drug-Resistant Pulmonary Tuberculosis

N -Acetyltransferase Genotypes and the Pharmacokinetics and Tolerability of para -Aminosalicylic Acid in Patients with Drug-Resistant Pulmonary Tuberculosis

Approaches for modeling within subject variability in pharmacometric count data analysis: dynamic inter-occasion variability and stochastic differential equations

New generalized poisson mixture model for bimodal count data with drug effect: An application to rodent brief‐access taste aversion experiments

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