Gross flow data from the current population survey, 1970-80 /

"PB82-174327."

The use of state and local labor market data from the Current population survey : the New York experience /

Presented at 14th annual meeting of the Association of Public Data Users, Washington, D.C., Oct. 24, 1989.

Analysis of population pharmacokinetic data using NONMEM and WinBUGS

The aim of this report is to describe the use of WinBUGS for two datasets that arise from typical population pharmacokinetic studies. The first dataset relates to gentamicin concentration-time data that arose as part of routine clinical care of 55 neonates. The second dataset incorporated data from 96 patients receiving enoxaparin. Both datasets were originally analyzed by using NONMEM. In the first instance, although NONMEM provided reasonable estimates of the fixed effects parameters it was unable to provide satisfactory estimates of the between-subject variance. In the second instance, the use of NONMEM resulted in the development of a successful model, albeit with limited available information on the between-subject variability of the pharmacokinetic parameters. WinBUGS was used to develop a model for both of these datasets. Model comparison for the enoxaparin dataset was performed by using the posterior distribution of the log-likelihood and a posterior predictive check. The use of WinBUGS supported the same structural models tried in NONMEM. For the gentamicin dataset a one-compartment model with intravenous infusion was developed, and the population parameters including the full between-subject variance-covariance matrix were available. Analysis of the enoxaparin dataset supported a two compartment model as superior to the one-compartment model, based on the posterior predictive check. Again, the full between-subject variance-covariance matrix parameters were available. Fully Bayesian approaches using MCMC methods, via WinBUGS, can offer added value for analysis of population pharmacokinetic data.

Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data

Background Our aim was to calculate the global burden of disease and risk factors for 2001, to examine regional trends from 1990 to 2001, and to provide a starting point for the analysis of the Disease Control Priorities Project (DCPP). Methods We calculated mortality, incidence, prevalence, and disability adjusted life years (DALYs) for 136 diseases and injuries, for seven income/geographic country groups. To assess trends, we re-estimated all-cause mortality for 1990 with the same methods as for 2001. We estimated mortality and disease burden attributable to 19 risk factors. Findings About 56 million people died in 2001. Of these, 10.6 million were children, 99% of whom lived in low-and-middle-income countries. More than half of child deaths in 2001 were attributable to acute respiratory infections, measles, diarrhoea, malaria, and HIV/AIDS. The ten leading diseases for global disease burden were perinatal conditions, lower respiratory infections, ischaemic heart disease, cerebrovascular disease, HIV/AIDS, diarrhoeal diseases, unipolar major depression, malaria, chronic obstructive pulmonary disease, and tuberculosis. There was a 20% reduction in global disease burden per head due to communicable, maternal, perinatal, and nutritional conditions between 1990 and 2001. Almost half the disease burden in low-and-middle-income countries is now from non-communicable diseases (disease burden per head in Sub-Saharan Africa and the low-and-middle-income countries of Europe and Central Asia increased between 1990 and 2001). Undernutrition remains the leading risk factor for health loss. An estimated 45% of global mortality and 36% of global disease burden are attributable to the joint hazardous effects of the 19 risk factors studied. Uncertainty in all-cause mortality estimates ranged from around 1% in high-income countries to 15-20% in Sub-Saharan Africa. Uncertainty was larger for mortality from specific diseases, and for incidence and prevalence of non-fatal outcomes. Interpretation Despite uncertainties about mortality and burden of disease estimates, our findings suggest that substantial gains in health have been achieved in most populations, countered by the HIV/AIDS epidemic in Sub-Saharan Africa and setbacks in adult mortality in countries of the former Soviet Union. our results on major disease, injury, and risk factor causes of loss of health, together with information on the cost-effectiveness of interventions, can assist in accelerating progress towards better health and reducing the persistent differentials in health between poor and rich countries.

Use of routine data for determination of the population pharmacokinetics and enteral biovailability of phenytoin in neonates and infants with seizures

Objective: To investigate the population pharmacokinetics and the enteral bioavailability of phenytoin in neonates and infants with seizures. Methods: Data (5 mg kg-1 day-1) from 83 patients were obtained retrospectively from the medical records following written ethical approval. A one-compartment model was fitted to the data using NONMEM with FOCE-interaction. Between-subject variability (BSV) and interoccasion variability (IOV) were modelled exponentially together with a log transform-both-sides exponential residual unexplained variance (RUV) model. Covariates in nested models were screened for significance (X2, 1, 0.01). Model validity was determined by bootstrapping with replacement (N=500 samples) from the dataset. Results: The parameters of final pharmacokinetic were: Clearance (L h-1) = 0.826.(current Weight [kg]/70)0.75.(1+0.0692.(Postnatal age [days]-11)); Volume of distribution (L) = 74.2.(current Weight [kg]/70); Enteral bioavailability = 0.76; Absorption rate constant (h-1) = 0.167. BSV for clearance and volume of distribution were 74.2% and 65.6%, respectively. The IOV in clearance was 54.4%. The RUV was 51.1%. Final model parameters deviated from mean bootstrap estimates by

A data structure for improved gp analysis via efficient computation and visualisation of population measures

Population measures for genetic programs are defined and analysed in an attempt to better understand the behaviour of genetic programming. Some measures are simple, but do not provide sufficient insight. The more meaningful ones are complex and take extra computation time. Here we present a unified view on the computation of population measures through an information hypertree (iTree). The iTree allows for a unified and efficient calculation of population measures via a basic tree traversal. © Springer-Verlag 2004.

On the Existence and Uniqueness of the Maximum Likelihood Estimators of Normal and Lognormal Population Parameters with Grouped Data

Lognormal distribution has abundant applications in various fields. In literature, most inferences on the two parameters of the lognormal distribution are based on Type-I censored sample data. However, exact measurements are not always attainable especially when the observation is below or above the detection limits, and only the numbers of measurements falling into predetermined intervals can be recorded instead. This is the so-called grouped data. In this paper, we will show the existence and uniqueness of the maximum likelihood estimators of the two parameters of the underlying lognormal distribution with Type-I censored data and grouped data. The proof was first established under the case of normal distribution and extended to the lognormal distribution through invariance property. The results are applied to estimate the median and mean of the lognormal population.

Using population-based critical care data to evaluate trauma outcomes

Copyright © 2015 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved. Acknowledgements We would like to thank the Scottish Intensive Care Society Audit Group (SICSAG) for providing the data for this study. Mr Jan Jansen is in receipt of an NHS Research Scotland fellowship which includes salary funding.

Using population-based critical care data to evaluate trauma outcomes

Copyright © 2015 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved. Acknowledgements We would like to thank the Scottish Intensive Care Society Audit Group (SICSAG) for providing the data for this study. Mr Jan Jansen is in receipt of an NHS Research Scotland fellowship which includes salary funding.

Cumulative live birth rates after one or more complete cycles of IVF : a population-based study of linked cycle data from 178 898 women

© The Author 2016. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Interactive visual analysis of population study data

Otto-von-Guericke-Universität Magdeburg, Fakultät für Informatik, Dissertation, 2015