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Mean Parameters (mean + parameter)
Selected AbstractsEstimation and Inference for a Spline-Enhanced Population Pharmacokinetic ModelBIOMETRICS, Issue 3 2002Lang Li Summary. This article is motivated by an application where subjects were dosed three times with the same drug and the drug concentration profiles appeared to be the lowest after the third dose. One possible explanation is that the pharmacokinetic (PK) parameters vary over time. Therefore, we consider population PK models with time-varying PK parameters. These time-varying PK parameters are modeled by natural cubic spline functions in the ordinary differential equations. Mean parameters, variance components, and smoothing parameters are jointly estimated by maximizing the double penalized log likelihood. Mean functions and their derivatives are obtained by the numerical solution of ordinary differential equations. The interpretation of PK parameters in the model and its flexibility are discussed. The proposed methods are illustrated by application to the data that motivated this article. The model's performance is evaluated through simulation. [source] High Moment Partial Sum Processes of Residuals in ARMA Models and their ApplicationsJOURNAL OF TIME SERIES ANALYSIS, Issue 1 2007Hao Yu Abstract., In this article, we study high moment partial sum processes based on residuals of a stationary autoregressive moving average (ARMA) model with known or unknown mean parameter. We show that they can be approximated in probability by the analogous processes which are obtained from the i.i.d. errors of the ARMA model. However, if a unknown mean parameter is used, there will be an additional term that depends on model parameters and a mean estimator. When properly normalized, this additional term will vanish. Thus the processes converge weakly to the same Gaussian processes as if the residuals were i.i.d. Applications to change-point problems and goodness-of-fit are considered, in particular, cumulative sum statistics for testing ARMA model structure changes and the Jarque,Bera omnibus statistic for testing normality of the unobservable error distribution of an ARMA model. [source] ORIGINAL ARTICLE: Investigation of the prediction accuracy of vancomycin concentrations determined by patient-specific parameters as estimated by Bayesian analysisJOURNAL OF CLINICAL PHARMACY & THERAPEUTICS, Issue 5 2010Y. Hiraki BSc Summary Background/Objective:, There have been many studies of therapeutic drug monitoring (TDM) of vancomycin (VCM) based on Bayesian analysis, but there have been no reports of the accuracy of prediction based on Bayesian-estimated patient-specific parameters. This study was conducted to compare the accuracy of prediction based on the population pharmacokinetic (PPK) method and Bayesian-estimated parameters. Method:, The subjects were 22 patients who were treated with VCM for MRSA infection and whose blood was sampled twice or more during the administration period. The concentrations between the blood samples were predicted based on the concentrations in the first blood samples based on the PPK method using mean parameters for the Japanese population and Bayesian-estimated patient-specific pharmacokinetic parameters. The mean prediction error (ME), mean absolute error (MAE) and root mean squared error (RMSE) were compared to examine the accuracy of prediction based on Bayesian-estimated patient-specific parameters. Results and discussion:, The mean measured VCM concentration was 10·43 ± 5·19 ,g/mL, whereas the mean concentration predicted based on the PPK method was 8·52 ± 4·34 ,g/mL, with an ME of ,1·91, MAE of 2·93 and RMSE of 3·21. The mean concentration predicted based on patient-specific parameters was 9·62 ± 4·95 ,g/mL with ME of ,0·81, MAE of 1·38 and RMSE of 1·74. The ME and MAE for the concentrations predicted using patient-specific parameters were smaller compared with those predicted using the PPK method (P = 0·0471 and 0·0003, respectively), indicating superior prediction with a significant difference between approaches. Conclusion:, Prediction using Bayesian estimates of patient-specific parameters was better than by the PPK method. However, when using patient-specific parameters it is still necessary to fully understand the clinical status of the patient and frequently determine VCM concentrations. [source] Color-based tumor tissue segmentation for the automated estimation of oral cancer parametersMICROSCOPY RESEARCH AND TECHNIQUE, Issue 1 2010Yung-nien Sun Abstract This article presents an automatic color-based feature extraction system for parameter estimation of oral cancer from optical microscopic images. The system first reduces image-to-image variations by means of color normalization. We then construct a database which consists of typical cancer images. The color parameters extracted from this database are then used in automated online sampling from oral cancer images. Principal component analysis is subsequently used to divide the color features into four tissue types. Each pixel in the cancer image is then classified into the corresponding tissue types based on the Mahalanobis distance. The aforementioned procedures are all fully automated; in particular, the automated sampling step greatly reduces the need for intensive labor in manual sampling and training. Experiments reveal high levels of consistency among the results achieved using the manual, semiautomatic, and fully automatic methods. Parameter comparisons between the four cancer stages are conducted, and only the mean parameters between early and late cancer stages are statistically different. In summary, the proposed system provides a useful and convenient tool for automatic segmentation and evaluation for stained biopsy samples of oral cancer. This tool can also be modified and applied to other tissue images with similar staining conditions. Microsc. Res. Tech. 2009. © 2009 Wiley-Liss, Inc. [source] Population modelling of the effect of inogatran, at thrombin inhibitor, on ex vivo coagulation time (APTT) in healthy subjects and patients with coronary artery diseaseBRITISH JOURNAL OF CLINICAL PHARMACOLOGY, Issue 1 2001Marie Cullberg Aims, The purpose of this study was to characterize the relationship between the degree of anticoagulation, assessed by APTT, and the plasma concentration of inogatran in healthy subjects and in patients with coronary artery disease. Methods, Data from five phase I studies in 78 healthy males and two phase II multicentre studies in 948 patients of both sexes with unstable angina pectoris or non-Q-wave myocardial infarction were evaluated. A total of 3296 pairs of concentration-APTT samples were obtained before, during, and after intravenous infusions of inogatran. Mixed effects modelling was used for population pharmacodynamic analysis of the drug effect and for describing the variability in baseline APTT. Results, The population mean baseline APTT was 29 s, but large variations between individuals (s.d. 3.6 s) were observed. The variability between studies (1.3 s) and centres (1.8 s) were of less importance, though statistically significant. APTT increased in a nonlinear manner with increasing inogatran concentration and the relationship was well described by a combined linear and Emax model. A significant part of the overall variability could be ascribed to the APTT reagent and equipment used at the different study centres. These method-dependent differences were compensated for by including the lower limit of the normal reference range as a covariate, affecting both baseline and Emax, in the model. For the typical healthy subject and patient, the method-corrected population mean parameters were: APTTbaseline 35 and 31 s, slope 8.0 and 5.8 s l µmol,1, Emax 36 and 34 s, and EC50 0.54 and 0.72 µmol l,1, respectively. The model predicted plasma concentration needed to double the APTT from the baseline value was 1.25 and 1.45 µmol l,1 in the healthy volunteer and patient, respectively. Conclusions, The nonlinear relationship between APTT and inogatran concentration in plasma was well described by a combined linear and Emax model. Pooling of data was made possible by incorporating a centre-specific characteristic of the assay method in the model. Patients had lower baseline APTT and appeared to have less pronounced effect of inogatran than young healthy subjects. [source] | |||||||||||||