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Effect Modeling (effect + modeling)
Kinds of Effect Modeling Selected AbstractsPopulation pharmacokinetics of isoniazid in the treatment of Mycobacterium tuberculosis among Asian and African elephants (Elephas maximus and Loxodonta africana)JOURNAL OF VETERINARY PHARMACOLOGY & THERAPEUTICS, Issue 1 2005J. N. MASLOW We recently described the clinical presentation and treatment of 18 elephants from six herds infected with TB. Treatment protocols and methods varied between herds to include both oral and rectal dosing using multiple drug doses and formulations. In this paper we present information regarding the pharmacokinetics (PK) of isoniazid (INH) in elephants and provide suggestions regarding initial treatment regimens. Forty-one elephants received INH daily by either oral or rectal administration with different formulations. Population PK analysis was performed using Non-linear Mixed Effect Modeling (NONMEM). Results of oral administration indicated that compared with premixed INH solution, the drug exposure was highest with a suspension prepared freshly with INH powder. When INH was concomitantly given as an admixture over food, Tmax was delayed and variability in drug absorption was significantly increased. Compared with oral administration, similar drug exposures were found when INH was dosed rectally. The data generated suggest that a starting dose of 7.5 mg/kg of INH is appropriate for initial TB treatment in elephants when premixed solution is administered directly into the oropharynx or rectal vault and 4 mg/kg are when INH is administered following immediate suspension from powdered form. [source] Advantages of mixed effects models over traditional ANOVA models in developmental studies: A worked example in a mouse model of fetal alcohol syndromeDEVELOPMENTAL PSYCHOBIOLOGY, Issue 7 2007Patricia E. Wainwright Abstract Developmental studies in animals often violate the assumption of statistical independence of observations due to the hierarchical nature of the data (i.e., pups cluster by litter, correlation of individual observations over time). Mixed effect modeling (MEM) provides a robust analytical approach for addressing problems associated with hierarchical data. This article compares the application of MEM to traditional ANOVA models within the context of a developmental study of prenatal ethanol exposure in mice. The results of the MEM analyses supported the ANOVA results in showing that a large proportion of the variability in both behavioral score and brain weight could be explained by ethanol. The MEM also identified that there were significant interactions between ethanol and litter size in relation to behavioral scores and brain weight. In addition, the longitudinal modeling approach using linear MEM allowed us to model for flexible weight gain over time, as well as to provide precise estimates of these effects, which would be difficult in repeated measures ANOVA. © 2007 Wiley Periodicals, Inc. Dev Psychobiol 49: 664,674, 2007. [source] Pharmacoepidemiologic investigation of a clonazepam-valproic acid interaction by mixed effect modeling using routine clinical pharmacokinetic data in Japanese patientsJOURNAL OF CLINICAL PHARMACY & THERAPEUTICS, Issue 6 2003E. Yukawa Summary Non-linear Mixed Effects Modeling (NONMEM) was used to estimate the effects of clonazepam,valproic acid interaction on clearance values using 576 serum levels collected from 317 pediatric and adult epileptic patients (age range, 0·3,32·6 years) during their clinical routine care. Patients received the administration of clonazepam and/or valproic acid. The final model describing clonazepam clearance was CL = 144·0 TBW,0·172 1·14VPA, where CL is total body clearance (mL/kg/h); TBW is total body weight (kg); VPA = 1 for concomitant administration of valproic acid and VPA = zero otherwise. The final model describing valproic acid clearance was CL (mL/kg/h) = 17·2 TBW,0·264 DOSE0·159 0·821CZP 0·896GEN, where DOSE is the daily dose of valproic acid (mg/kg/day); CZP = 1 for concomitant administration of clonazepam and CZP = zero otherwise; GEN = 1 for female and GEN = zero otherwise. Concomitant administration of clonazepam and valproic acid resulted in a 14% increase in clonazepam clearance, and a 17·9% decrease in valproic acid clearance. [source] Pharmacokinetic prediction for intravenous ,-lactam antibiotics in pediatric patientsJOURNAL OF PHARMACEUTICAL SCIENCES, Issue 11 2007Kenji Shimamura Abstract A method for predicting pharmacokinetics in pediatric patients for intravenous ,-lactam antibiotics is proposed. We focused on the allometric relationships of pharmacokinetic parameters with individual body weights (BW) in human including healthy adults and pediatric patients. Drug concentration data for 15 intravenous ,-lactam antibiotics were collected retrospectively from the published articles and the individual pharmacokinetic parameters were re-calculated. A mixed effect modeling (MEM) was applied for the allometric relationship for those ,-lactam antibiotics, and mean and variances of inter-drug variability for the allometric parameters and also variance for intra-drug (residual) variability were estimated. Then drug-specific allometric parameters were estimated by an empirical Bayesian method using the pharmacokinetic parameters for a drug only in healthy adults as observations, and finally the individual pharmacokinetic parameters in pediatric patients were predicted. The predictability of the method was evaluated by the leave-one-out method. We also demonstrated a method for simulating plasma concentration,time profiles in pediatric patients, and the predicted time,course curves generally coincided well with the actual plasma concentration data for the tested drugs. © 2007 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 96: 3125,3139, 2007 [source] Population pharmacokinetics of pyrazinamide in elephantsJOURNAL OF VETERINARY PHARMACOLOGY & THERAPEUTICS, Issue 5 2005M. ZHU This study was undertaken to characterize the population pharmacokinetics (PK), therapeutic dose, and preferred route of administration for pyrazinamide (PZA) in elephants. Twenty-three African (Loxodonta africana) and Asian (Elephas maximus) elephants infected with or in contact with others culture positive for Mycobacterium tuberculosis were dosed under treatment conditions. PZA was dosed daily at 20,30 mg/kg via oral (fasting or nonfasting state) or rectal (enema or suppository) administration. Blood samples were collected 0,24 h postdose. Population PK was estimated using nonlinear mixed effect modeling. Drug absorption was rapid with Tmax at or before 2 h regardless of the method of drug administration. Cmax at a mean dose of 25.6 (±4.6) mg/kg was 19.6 (±9.5 ,g/mL) for PZA given orally under fasting conditions. Under nonfasting conditions at a mean dose of 26.1 ± 4.2 mg/kg, Cmax was 25% (4.87 ± 4.89 ,g/mL) and area under concentration curve (AUC) was 30% of the values observed under fasting conditions. Mean rectal dose of 32.6 ± 15.2 mg/kg yielded Cmax of 12.3 ± 6.3 ,g/mL, but comparable AUC to PZA administered orally while fasting. Both oral and rectal administration of PZA appeared to be acceptable and oral dosing is preferred because of the higher Cmax and lower inter-subject variability. A starting dose of 30 mg/kg is recommended with drug monitoring between 1 and 2 h postdose. Higher doses may be required if the achieved Cmax values are below the recommended 20,50 ,g/mL range. [source] Meta-analysis of pharmacokinetic data of veterinary drugs using the Food Animal Residue Avoidance Databank: oxytetracycline and procaine penicillin GJOURNAL OF VETERINARY PHARMACOLOGY & THERAPEUTICS, Issue 5 2004A. L. Craigmill Investigators frequently face the quandary of how to interpret the oftentimes disparate pharmacokinetic parameter values reported in the literature. Combining of data from multiple studies (meta-analysis) is a useful tool in pharmacokinetics. Few studies have explored the use of meta-analysis for veterinary species. Even fewer studies have explored the potential strengths and weaknesses of the various methods of performing a meta-analysis. Therefore, in this study we performed a meta-analysis for oxytetracycline (OTC) and procaine penicillin G (PPG) given intramuscularly to cattle. The analysis included 28 individual data sets from 18 published papers for PPG (288 data points), and 41 individual data sets from 25 published papers for OTC (489 data points). Three methods were used to calculate the parameters. The first was a simple statistical analysis of the parameter values reported in each paper. The second method was a standard Two-Stage Method (TSM) using the mean concentration vs. time data extracted from each paper. The third method was the use of nonlinear mixed effect modeling (NMEM) of the concentration vs. time data reported in the various papers, treating the mean data as if each set came from an individual animal. The results of this evaluation indicate that all three methods generate comparable mean parameter estimates for OTC and PPG. The only significant difference noted was for OTC absorption half-lives taken from the published literature, a difference attributable to the use of an alternative method of parameter calculation. The NMEM procedure offers the possibility of including covariates such as dose, age, and weight. In this study the covariates did not influence the derived parameters. A combination approach to meta-analysis of published mean data is recommended, where the TSM is the first step, followed by the NMEM approach. [source] Generalized Hierarchical Multivariate CAR Models for Areal DataBIOMETRICS, Issue 4 2005Xiaoping Jin Summary In the fields of medicine and public health, a common application of areal data models is the study of geographical patterns of disease. When we have several measurements recorded at each spatial location (for example, information on p, 2 diseases from the same population groups or regions), we need to consider multivariate areal data models in order to handle the dependence among the multivariate components as well as the spatial dependence between sites. In this article, we propose a flexible new class of generalized multivariate conditionally autoregressive (GMCAR) models for areal data, and show how it enriches the MCAR class. Our approach differs from earlier ones in that it directly specifies the joint distribution for a multivariate Markov random field (MRF) through the specification of simpler conditional and marginal models. This in turn leads to a significant reduction in the computational burden in hierarchical spatial random effect modeling, where posterior summaries are computed using Markov chain Monte Carlo (MCMC). We compare our approach with existing MCAR models in the literature via simulation, using average mean square error (AMSE) and a convenient hierarchical model selection criterion, the deviance information criterion (DIC; Spiegelhalter et al., 2002, Journal of the Royal Statistical Society, Series B64, 583,639). Finally, we offer a real-data application of our proposed GMCAR approach that models lung and esophagus cancer death rates during 1991,1998 in Minnesota counties. [source] | ||||||||||