Empirical Bayesian Estimation Research Articles

Estimating sensitivity indices based on Gaussian process metamodels with compactly supported correlation functions

Specific formulae are derived for quadrature-based estimators of global sensitivity indices when the unknown function can be modeled by a regression plus stationary Gaussian process using the Gaussian, Bohman, or cubic correlation functions. Estimation formulae are derived for the computation of process-based Bayesian and empirical Bayesian estimates of global sensitivity indices when the observed data are the function values corrupted by noise. It is shown how to restrict the parameter space for the compactly supported Bohman and cubic correlation functions so that (at least) a given proportion of the training data correlation entries are zero. This feature is important in the situation where the set of training data is large. The estimation methods are illustrated and compared via examples.

Empirical Bayesian estimation of location of the Cauchy distribution

The Cauchy distribution has a heavy tail so that the estimation of its location becomes complex. An empirical Bayesian estimation of the location for the Cauchy distribution is proposed in this article. The new estimator is not only computationally easy, but also the most efficient estimator, compared with existing estimators in the literature.

Pretreatment dietary intake is associated with tumor suppressor DNA methylation in head and neck squamous cell carcinomas

Diet is associated with cancer prognosis, including head and neck cancer (HNC), and has been hypothesized to influence epigenetic state by determining the availability of functional groups involved in the modification of DNA and histone proteins. The goal of this study was to describe the association between pretreatment diet and HNC tumor DNA methylation. Information on usual pretreatment food and nutrient intake was estimated via food frequency questionnaire (FFQ) on 49 HNC cases. Tumor DNA methylation patterns were assessed using the Illumina Goldengate Methylation Cancer Panel. First, a methylation score, the sum of individual hypermethylated tumor suppressor associated CpG sites, was calculated and associated with dietary intake of micronutrients involved in one-carbon metabolism and antioxidant activity, and food groups abundant in these nutrients. Second, gene specific analyses using linear modeling with empirical Bayesian variance estimation were conducted to identify if methylation at individual CpG sites was associated with diet. All models were controlled for age, sex, smoking, alcohol and HPV status. Individuals reporting in the highest quartile of folate, vitamin B12 and vitamin A intake, compared with those in the lowest quartile, showed significantly less tumor suppressor gene methylation, as did patients reporting the highest cruciferous vegetable intake. Gene specific analyses identified differential associations between DNA methylation and vitamin B12 and vitamin A intake when stratifying by HPV status. These preliminary results suggest that intake of folate, vitamin A and vitamin B12 may be associated with the tumor DNA methylation profile in HNC and enhance tumor suppression.

Identifying Areas of High Risk of Human Exposure to Coccidioidomycosis in Texas Using Serology Data from Dogs

Coccidioidomycosis or Valley Fever (VF) is an emerging soil-borne fungal zoonosis affecting humans and animals. Most non-human cases of VF are found in dogs, which we hypothesize may serve as sentinels for estimating the human exposure risk. The objective of this study is to use the spatial and temporal distribution and clusters of dogs seropositive for VF to define the geographic area in Texas where VF is endemic, and thus presents a higher risk of exposure to humans. The included specimens were seropositive dogs tested at a major diagnostic laboratory between 1999 and 2009. Data were aggregated by zip code and smoothed by empirical Bayesian estimation to develop an isopleth map of VF seropositive rates using kriging. Clusters of seropositive dogs were identified using the spatial scan test. Both the isopleth map and the scan test identified an area with a high rate of VF-seropositive dogs in the western and southwestern parts of Texas (relative risk = 31). This location overlapped an area that was previously identified as a potential endemic region based on human surveys. Together, these data suggest that dogs may serve as sentinels for estimating the risk of human exposure to VF.

Serum IgG Responses and Seroconversion Patterns to Cryptosporidium gp15 among Children in a Birth Cohort in South India

The correlates of protective immunity to cryptosporidiosis are not well understood. This study was conducted to assess the effect of maternal serum IgG against Cryptosporidium gp15 on responses to this antigen in children with (cases) and without (controls) PCR-confirmed cryptosporidial diarrhea. Maternal sera (n = 129) and sera from cases (n = 39) and controls (n = 90) collected at 3.5, 9, and 24 months of age were tested for serum IgG against Cryptosporidium gp15 by enzyme-linked immunosorbent assay (ELISA). Seroconversion patterns were evaluated by estimating probabilities of seroconversion along three time points based on the transition pathways by using a first-order Markov chain process and empirical Bayesian estimates. There was no difference in serum IgG levels or seropositivity rates to gp15 between cases and controls across all time points in children or in IgG levels to this antigen between mothers of cases and controls. The most common transition pathway can be described as a seronegative child at 3.5 months who seroconverts at 9 months and remains seropositive at 24 months. This pattern remained stable irrespective of the serological status of the mother or the case or control status of the child. Children were most likely to be exposed to Cryptosporidium for the first time between the ages of 3 and 9 months, and most of the children seroconverted by 24 months. The high degree of seroconversion among control children is suggestive of high rates of asymptomatic transmission in this region.

Pharmacokinetics of doripenem in CSF of patients with non-inflamed meninges

To investigate intact blood-brain barrier (BBB) penetration by doripenem and characterize doripenem pharmacokinetics in CSF using a pharmacokinetic model. Thirty-eight neurological patients with no active neurological disease or CNS infection received a single 500 mg doripenem dose before pump implantation surgery, or lumbar puncture, for intrathecal baclofen administration. In most cases single CSF and blood samples were collected per patient and analysed for doripenem with HPLC. A two-stage pharmacokinetic analysis was performed to estimate: (i) empirical Bayesian estimates (EBEs) of individual doripenem plasma pharmacokinetic parameters, using plasma doripenem concentrations and literature population priors for a two-compartment model; and (ii) doripenem CSF pharmacokinetic parameters using simulated plasma concentrations from stage (i) as a forcing function. The mean values of the structural model parameters, k(CSF) (distribution rate constant) and PC (CSF/plasma partition coefficient), and the residual variability were estimated. The mean estimates of the parameters were k(CSF)= 0.105 h(-1) and PC= 0.053, corresponding to mean steady-state doripenem CSF concentrations of 0.20 mg/L and 0.40 mg/L for regimens of 3 × 500 mg daily and 3 × 1000 mg daily, respectively, and a mean equilibrium half-life of 6.6 h. The model was validated internally using a visual predictive check (VPC) and bootstrap. Simulating two dosing scenarios gave doripenem levels in the CSF above or close to the literature MIC values. The present NONMEM software analysis shows that doripenem crosses intact BBB significantly and suggests that the drug should be further evaluated as a candidate to treat certain CNS infections, since drug penetration through BBB is enhanced by meningeal inflammation.

Population pharmacokinetics of ciclosporin in haematopoietic allogeneic stem cell transplantation with emphasis on limited sampling strategy

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • The population pharmacokinetics and limited sampling strategies for ciclosporin monitoring have been extensively studied in renal and liver transplant recipients. Little is known about the pharmacokinetics of ciclosporin in patients undergoing haematopoietic allogeneic stem cell transplantation (HSCT). • It is anticipated that there is a difference in pharmacokinetics in patients after kidney or liver transplantation compared with patients undergoing stem cell transplantation, because of mucositis and interacting drugs (e.g. fluconazole). • Data on the pharmacokinetics of ciclosporin and the relationship between its systemic exposure, as reflected by the area under the curve (AUC), and the biological effect as graft vs. host-disease (GVHD) prophylaxis and graft vs. tumour (GVT) response are scarce in patients after HSCT. WHAT THIS STUDY ADDS • A pharmacokinetic model was developed for orally and intravenously administered ciclosporin, enabling an adequate estimate of the systemic exposure of ciclosporin in patients after HSCT. A limited sampling strategy was tested that may serve as a tool to study the optimum systemic exposure (AUC) of ciclosporin in HSCT to prevent GVHD but establish adequate GVT response and to guide therapeutic drug monitoring. AIM To develop a population pharmacokinetic model of ciclosporin (CsA) in haematopoietic allogeneic stem cell transplantation to facilitate a limited sampling strategy to determine systemic exposure (area under the curve [AUC]), in order to optimize CsA therapy in this patient population. METHODS The pharmacokinetics of CsA were investigated prospectively in 20 patients following allogeneic haematopoietic stem cell transplantation (HSCT). CsA was given twice daily, as a 3 h i.v. infusion starting at day 1 of the conditioning scheme, and orally later on, when oral intake was well tolerated. Fluconazole was given as antimycotic prophylaxis. Pharmacokinetic parameter estimation was performed using nonlinear mixed effect modelling as implemented in the NONMEM program. A first order absorption model with lag time was compared with Erlang frequency distribution and Weibull distribution models. The influence of demographic variables on the individual empirical Bayesian estimates of clearance and distribution volume was tested. Subsequently two limited sampling strategies (LSS) were evaluated: posterior Bayesian fitting and limited sampling equations. RESULTS Twenty patients were included and 435 samples were collected after i.v. and oral administration of CsA. A two compartment model with first order absorption best described the data. Clearance (CL) was 21.9 l h(-1) (relative standard deviation [RSD]± 5.2%) with an inter-individual variability of 21%. The central volume of distribution (V(c) ) was 18.3 l (RSD ± 8.7%) with an inter-individual variability of 29%. Bioavailability (F) was 0.71 (RSD ± 9.9%) with and inter-individual variability of 25% and lag time (t(lag) ) was 0.44 h (RSD 5.5%). Weight, body surface area, haematocrit, albumin, ALAT and ASAT had no significant influence on pharmacokinetic parameters. The best multiple point combination for posterior Bayesian fitting, in terms of estimating systemic CsA exposure, appeared to be C0 + C2 + C3. Two selected LSS two time point equations and all selected three and four time point equations predicted de all AUC(0,12 h) within 15% bias and prediction. CONCLUSIONS The i.v. and oralcurves were best described with a two compartment model with first-order absorption with lag time. With the Bayesian estimators from this model, the area under the concentration-time curve in HSCT patients taking fluconazole can be estimated with only three blood samples (0, 2, 3 h) with a bias of 1% and precision of 4%.

Clusters of leprosy transmission and of late diagnosis in a highly endemic area in Brazil: focus on different spatial analysis approaches

The Brazilian National Hansen's Disease Control Program recently identified clusters with high disease transmission. Herein, we present different spatial analytical approaches to define highly vulnerable areas in one of these clusters. The study area included 373 municipalities in the four Brazilian states Maranhão, Pará, Tocantins and Piauí. Spatial analysis was based on municipalities as the observation unit, considering the following disease indicators: (i) rate of new cases/100,000 population, (ii) rate of cases <15 years/100,000 population, (iii) new cases with grade-2 disability/100,000 population and (iv) proportion of new cases with grade-2 disabilities. We performed descriptive spatial analysis, local empirical Bayesian analysis and spatial scan statistic. A total of 254 (68.0%) municipalities were classified as hyperendemic (mean annual detection rates >40 cases/100,000 inhabitants). There was a concentration of municipalities with higher detection rates in Pará and in the center of Maranhão. Spatial scan statistic identified 23 likely clusters of new leprosy case detection rates, most of them localized in these two states. These clusters included only 32% of the total population, but 55.4% of new leprosy cases. We also identified 16 significant clusters for the detection rate <15 years and 11 likely clusters of new cases with grade-2. Several clusters of new cases with grade-2/population overlap with those of new cases detection and detection of children <15 years of age. The proportion of new cases with grade-2 did not reveal any significant clusters. Several municipality clusters for high leprosy transmission and late diagnosis were identified in an endemic area using different statistical approaches. Spatial scan statistic is adequate to validate and confirm high-risk leprosy areas for transmission and late diagnosis, identified using descriptive spatial analysis and using local empirical Bayesian method. National and State leprosy control programs urgently need to intensify control actions in these highly vulnerable municipalities.

The Study about Autumobile Insurance Based on Linear Empirical Bayesian Estimation

Automobile insurance is one of the most popular research areas, and there are a lot of different methods for it .We uses linear empirical Bayesian estimation for the study of automobile insurance, giving the estimator of the policy’s future claim size. Thus, a new point of view is given on the pricing of automobile insurance.

Geographical mapping and Bayesian spatial modeling of malaria incidence in Sistan and Baluchistan province, Iran

Objective To present the geographical map of malaria and identify some of the important environmental factors of this disease in Sistan and Baluchistan province, Iran. Methods We used the registered malaria data to compute the standard incidence rates (SIRs) of malaria in different areas of Sistan and Baluchistan province for a nine-year period (from 2001 to 2009). Statistical analyses consisted of two different parts: geographical mapping of malaria incidence rates, and modeling the environmental factors. The empirical Bayesian estimates of malaria SIRs were utilized for geographical mapping of malaria and a Poisson random effects model was used for assessing the effect of environmental factors on malaria SIRs. Results In general, 64 926 new cases of malaria were registered in Sistan and Baluchistan Province from 2001 to 2009. Among them, 42 695 patients (65.8%) were male and 22 231 patients (34.2%) were female. Modeling the environmental factors showed that malaria incidence rates had positive relationship with humidity, elevation, average minimum temperature and average maximum temperature, while rainfall had negative effect on malaria SIRs in this province. Conclusions The results of the present study reveals that malaria is still a serious health problem in Sistan and Baluchistan province, Iran. Geographical map and related environmental factors of malaria can help the health policy makers to intervene in high risk areas more efficiently and allocate the resources in a proper manner.

The role of gender in predicting for reduced drug clearance and increased toxicity in non-small cell lung cancer (NSCLC) patients receiving carboplatin and paclitaxel.

7557 Background: There is wide variability in toxicity from chemotherapy attributed to multiple factors including pharmacogenomic (PG), ethnic and nutritional differences. Gender differences in tolerance of anticancer drugs have also been described. The aim of this ongoing study was to investigate predictors of toxicity in advanced NSCLC patients receiving palliative chemotherapy. Methods: Stage III-IV NSCLC patients, receiving paclitaxel (175 mg/m2) and carboplatin (AUC 6) 3 weekly were enrolled. Demographic, nutritional and inflammatory data were collected at baseline. Plasma samples were collected using a limited sampling strategy and drug concentrations measured using ICP-MS for carboplatin and LC-MS for paclitaxel. Empiric Bayesian estimates of clearance (CL) were obtained using population pharmacokinetic (PK) analyses and NONMEM software. Toxicities, monitored weekly in cycle 1, were graded according to CTCAEv3.0 and correlated with the PK data. Results: Forty-four male and 20 female patients were recruited. Median age was 63 years for both men and women. Women experienced more peripheral sensory neuropathy (73.7% vs. 33.3%, p = 0.003), stomatitis (26.3% vs. 7.1%, p = 0.04) and grade 2-3 vomiting (26.3% vs. 7.1%, p = 0.04) than men. There were no significant gender differences in haematological toxicities. PK analyses showed that women had lower paclitaxel CL (10.5 L/h vs. 13.0 L/h, p < 0.0001) and carboplatin CL (7.3 L/h vs. 9.3 L/h, p < 0.0001) than men. Median survival was longer for women (18.4 vs. 16.0 months), although this was not statistically significant. Asians had significant slower clearance of paclitaxel than Caucasians (11.1 L/h vs. 12.7 L/h, p = 0.044). Conclusions: Women with NSCLC had lower clearance of both carboplatin and paclitaxel than men, which was associated with an increased incidence of non-haematological toxicity. The causes for these differences should be explored with PG and metabonomic analyses.

Performance of Safety Indicators in Identification of Black Spots on Two-Lane Rural Roads

Defining a black spot is not as perceptual as it may appear. Unusually high crash counts do not necessarily indicate a real safety problem. When black spots on two-lane rural roads with low and medium traffic volumes are being identified, a reduced number of observed crashes is a critical issue that can emphasize weak results obtained with various safety indicators and checks. It is generally assumed that an empirical Bayesian (EB) estimate represents the best approach for the identification of black spots, but it is difficult to define quantitatively the precision of estimates that are arrived at with different procedures. Because traffic volume, segment length, and crash observation period are critical issues in the identification of black spots and can emphasize the quality of the results obtained with safety indicators and checks, definition is needed of the limits and errors that can be expected when inadequate indicators and procedures are used to identify black spots on two-lane rural roads. A Monte Carlo simulation was used to produce theoretical crash data similar to empirical data, and the data were used to define a priori hazardous sites and, therefore, to assess whether a method could correctly identify such sites. The accuracy and efficiency of procedures are compared on the basis of observed frequency of crashes, crash rate, EB estimation, and the potential for safety improvement (PSI). As a general rule, even if some arrangements can mitigate the lower performance of some indicators, the best practice is to use the indicators based on the EB estimation PSI to identify true positives.

Evaluation of Safety Identification Methods for Low-Volume Roads Using Monte Carlo Simulation

Two-lane rural roads characterized by low traffic volume (fewer than 3,000 vehicles per day) represent a specific road typology of interest in safety analysis. Correct identification of black spots (sites in which the expected number of crashes is abnormally high) is crucial for the road administration when the location and ranking of improvements are chosen. Simply observing unusually high crash counts does not necessarily indicate a true safety problem. In black spot identification on two-lane rural roads with low traffic volume, the reduced number of observed crashes is a critical issue that can emphasize weak results carried out with different safety indicators and control checks. Generally it is assumed that the empirical Bayesian estimate represents the best approach for black spot identification, but the precision of the estimation among different procedures is difficult to define quantitatively. The simulation approach is able to identify crash data similar to empirical data with the unique possibility to define a priori hazardous sites and therefore assess whether a method can correctly identify them. The Monte Carlo simulation presented here was able to define the accuracy and efficiency of the procedures on the basis of observed frequency of crashes, crash rate, empirical Bayes estimation, and potential for safety improvement. The results show that an appropriate selection of exposure values can mitigate the lower performance of some indicators, but the best practice is to use the empirical Bayes approach to identify true positives.

Population Pharmacokinetic Study of Cyclosporine Based on NONMEM in Chinese Liver Transplant Recipients

A population pharmacokinetic study of cyclosporine (CsA) was performed in liver transplant recipients. A total of 3731 retrospective drug monitoring data points at predose (C0) and 2 hours postdose (C2) were collected from 124 liver transplant recipients receiving CsA microemulsion. Population pharmacokinetic analysis was performed using the program NONMEM (nonlinear mixed-effect modeling). Various covariates potentially related to CsA pharmacokinetics were explored, and the final model was validated by a bootstrap method and by assessing the predictive performance using empiric Bayesian estimates. A one-compartment model with first-order absorption was considered. Population parameters of apparent clearance (CL/F) and volume of distribution were estimated as 23.1 L/h and 105 L, respectively. CL/F was influenced by four covariates: duration of CsA therapy (DT), hematocrit (HCT), and concurrent prednisone dose (PR). The final model for CL/F was fitted as follows: CL/F = 23.1 + 0.5 × (DT/200) - 0.07 × HCT + 0.04 × PR. The interindividual variability in CL/F, volume of distribution, and Ka calculated as coefficient of variation were 15.1%, 9.3%, and 66.0%, respectively. The intraindividual variability was 18.6%. The model fitted well with the observed data, and the bootstrap method guaranteed robustness of the population pharmacokinetic study model. Model validation was performed by a visual predictive check. Moreover, simulation was conducted to facilitate the individualized treatment based on patient information and the final model. The model to characterize population pharmacokinetic study of CsA provided better clinical individualization of CsA dosing in liver transplant recipients based on patient information and to assess patients' suitability for CsA therapy.

The use of the SAEM algorithm in MONOLIX software for estimation of population pharmacokinetic-pharmacodynamic-viral dynamics parameters of maraviroc in asymptomatic HIV subjects

Using simulated viral load data for a given maraviroc monotherapy study design, the feasibility of different algorithms to perform parameter estimation for a pharmacokinetic-pharmacodynamic-viral dynamics (PKPD-VD) model was assessed. The assessed algorithms are the first-order conditional estimation method with interaction (FOCEI) implemented in NONMEM VI and the SAEM algorithm implemented in MONOLIX version 2.4. Simulated data were also used to test if an effect compartment and/or a lag time could be distinguished to describe an observed delay in onset of viral inhibition using SAEM. The preferred model was then used to describe the observed maraviroc monotherapy plasma concentration and viral load data using SAEM. In this last step, three modelling approaches were compared; (i) sequential PKPD-VD with fixed individual Empirical Bayesian Estimates (EBE) for PK, (ii) sequential PKPD-VD with fixed population PK parameters and including concentrations, and (iii) simultaneous PKPD-VD. Using FOCEI, many convergence problems (56%) were experienced with fitting the sequential PKPD-VD model to the simulated data. For the sequential modelling approach, SAEM (with default settings) took less time to generate population and individual estimates including diagnostics than with FOCEI without diagnostics. For the given maraviroc monotherapy sampling design, it was difficult to separate the viral dynamics system delay from a pharmacokinetic distributional delay or delay due to receptor binding and subsequent cellular signalling. The preferred model included a viral load lag time without inter-individual variability. Parameter estimates from the SAEM analysis of observed data were comparable among the three modelling approaches. For the sequential methods, computation time is approximately 25% less when fixing individual EBE of PK parameters with omission of the concentration data compared with fixed population PK parameters and retention of concentration data in the PD-VD estimation step. Computation times were similar for the sequential method with fixed population PK parameters and the simultaneous PKPD-VD modelling approach. The current analysis demonstrated that the SAEM algorithm in MONOLIX is useful for fitting complex mechanistic models requiring multiple differential equations. The SAEM algorithm allowed simultaneous estimation of PKPD and viral dynamics parameters, as well as investigation of different model sub-components during the model building process. This was not possible with the FOCEI method (NONMEM version VI or below). SAEM provides a more feasible alternative to FOCEI when facing lengthy computation times and convergence problems with complex models.

Variance-Component Based Sparse Signal Reconstruction and Model Selection

We propose a variance-component probabilistic model for sparse signal reconstruction and model selection. The measurements follow an underdetermined linear model, where the unknown regression vector (signal) is sparse or approximately sparse and noise covariance matrix is known up to a constant. The signal is composed of two disjoint parts: a part with significant signal elements and the complementary part with insignificant signal elements that have zero or small values. We assign distinct variance components to the candidates for the significant signal elements and a single variance component to the rest of the signal; consequently, the dimension of our model's parameter space is proportional to the assumed sparsity level of the signal. We derive a generalized maximum-likelihood (GML) rule for selecting the most efficient parameter assignment and signal representation that strikes a balance between the accuracy of data fit and compactness of the parameterization. We prove that, under mild conditions, the GML-optimal index set of the distinct variance components coincides with the support set of the sparsest solution to the underlying underdetermined linear system. Finally, we propose an expansion-compression variance-component based method (ExCoV) that aims at maximizing the GML objective function and provides an approximate GML estimate of the significant signal element set and an empirical Bayesian signal estimate. The ExCoV method is automatic and demands no prior knowledge about signal-sparsity or measurement-noise levels. We also develop a computationally and memory efficient approximate ExCoV scheme suitable for large-scale problems, apply the proposed methods to reconstruct one- and two-dimensional signals from compressive samples, and demonstrate their reconstruction performance via numerical simulations. Compared with the competing approaches, our schemes perform particularly well in challenging scenarios where the noise is large or the number of measurements is small.

Empirical Bayesian estimation in graphical analysis: a voxel-based approach for the determination of the volume of distribution in PET studies

Total volume of distribution (V(T)) determined by graphical analysis (GA) of PET data suffers from a noise-dependent bias. Likelihood estimation in GA (LEGA) eliminates this bias at the region of interest (ROI) level, but at voxel noise levels, the variance of estimators is high, yielding noisy images. We hypothesized that incorporating LEGA V(T) estimation in a Bayesian framework would shrink estimators towards prior means, reducing variability and producing meaningful and useful voxel images. Empirical Bayesian estimation in GA (EBEGA) determines prior distributions using a two-step k-means clustering of voxel activity. Results obtained on eight [(11)C]-DASB studies are compared with estimators computed by ROI-based LEGA. EBEGA reproduces the results obtained by ROI LEGA while providing low-variability V(T) images. Correlation coefficients between average EBEGA V(T) and corresponding ROI LEGA V(T) range from 0.963 to 0.994. EBEGA is a fully automatic and general approach that can be applied to voxel-level V(T) image creation and to any modeling strategy to reduce voxel-level estimation variability without prefiltering of the PET data.

Double shrinkage empirical Bayesian estimation for unknown and unequal variances

In this paper, we construct a point estimator when assuming unequal and unknown variances by using the empirical Bayes approach in the classical normal mean problem. The proposed estimator shrinks both means and variances, and is thus called the double shrinkage estimator. Extensive numerical studies indicate that the double shrinkage estimator has lower Bayes risk than the estimator which shrinks the means alone, and the naive estimator which has no shrinkage at all. We further use a spike-in data set to assess different estimating procedures. It turns out that our proposed estimator performs the best and is thus strongly recommended for applications. AMS 2000 subject classifications: Primary 60K35, 60K35; secondary 60K35.

Estimating Instantaneous Irregularity of Neuronal Firing

Cortical neurons in vivo had been regarded as Poisson spike generators that convey no information other than the rate of random firing. Recently, using a metric for analyzing local variation of interspike intervals, researchers have found that individual neurons express specific patterns in generating spikes, which may symbolically be termed regular, random, or bursty, rather invariantly in time. In order to study the dynamics of firing patterns in greater detail, we propose here a Bayesian method for estimating firing irregularity and the firing rate simultaneously for a given spike sequence, and we implement an algorithm that may render the empirical Bayesian estimation practicable for data comprising a large number of spikes. Application of this method to electrophysiological data revealed a subtle correlation between the degree of firing irregularity and the firing rate for individual neurons. Irregularity of firing did not deviate greatly around the low degree of dependence on the firing rate and remained practically unchanged for individual neurons in the cortical areas V1 and MT, whereas it fluctuated greatly in the lateral geniculate nucleus of the thalamus. This indicates the presence and absence of autocontrolling mechanisms for maintaining patterns of firing in the cortex and thalamus, respectively.

Pharmacokinetics and dose requirements of factor VIII over the age range 3–74 years

The three aims of this investigation were (1) to develop a population pharmacokinetic (PK) model for factor VIII (FVIII) in haemophilia A patients, with estimates of inter-occasion and inter-individual variance, (2) to investigate whether appropriate dosing of FVIII for regular prophylaxis can be calculated according to patient characteristics, and (3) to present dosing recommendations for initiating prophylactic treatment. A population PK model was developed using data from four PK studies on patients aged 7-74 years. The model was tested on sparse FVIII data from 42 outpatient visits by haemophilia prophylaxis patients aged 3-66 years. Dose requirements for prophylaxis were calculated both according to the population model and from empirical Bayesian estimates of FVIII PK in the individual patients. The study data were well characterised by a two-compartment PK model. Body weight, age and type of FVIII preparation (plasma-derived or recombinant) were identified as significant covariates. Inter-occasion variance was lower than inter-individual variance for both clearance and volume of the central compartment. The model could reasonably predict FVIII PK in the sparse clinical data. Model-predicted doses (based on age and body weight) to maintain a recommended 0.01 U/mL trough level of FVIII with administration on alternate days started at around 60 U/kg in the small children, decreasing to 10 U/kg or less in middle age. However, "true" dose requirements, as estimated from individual PK parameter data, showed a much greater variation. Appropriate dosing of FVIII for prophylactic treatment cannot be calculated only from body weight and/or age. However, plausible starting doses for most patients would be 1,000 U every other day. FVIII levels should then be checked for dose adjustment.