James H. Ware

Bio: James H. Ware is an academic researcher from United States Environmental Protection Agency. The author has contributed to research in topics: Air pollutant concentrations & Probability distribution. The author has an hindex of 9, co-authored 10 publications receiving 9313 citations. Previous affiliations of James H. Ware include Harvard University.

Random-effects models for longitudinal data

Abstract: Models for the analysis of longitudinal data must recognize the relationship between serial observations on the same unit. Multivariate models with general covariance structure are often difficult to apply to highly unbalanced data, whereas two-stage random-effects models can be used easily. In two-stage models, the probability distributions for the response vectors of different individuals belong to a single family, but some random-effects parameters vary across individuals, with a distribution specified at the second stage. A general family of models is discussed, which includes both growth models and repeated-measures models as special cases. A unified approach to fitting these models, based on a combination of empirical Bayes and maximum likelihood estimation of model parameters and using the EM algorithm, is discussed. Two examples are taken from a current epidemiological study of the health effects of air pollution.

Effects of inhalable particles on respiratory health of children.

Abstract: Results are presented from a second cross-sectional assessment of the association of air pollution with chronic respiratory health of children participating in the Six Cities Study of Air Pollution and Health. Air pollution measurements collected at quality-controlled monitoring stations included total suspended particulates (TSP), particulate matter less than 15 microns (PM15) and 2.5 microns (PM2.5) aerodynamic diameter, fine fraction aerosol sulfate (FSO4), SO2, O3, and No2. Reported rates of chronic cough, bronchitis, and chest illness during the 1980-1981 school year were positively associated with all measures of particulate pollution (TSP, PM15, PM2.5, and FSO4) and positively but less strongly associated with concentrations of two of the gases (SO2 and NO2). Frequency of earache also tended to be associated with particulate concentrations, but no associations were found with asthma, persistent wheeze, hay fever, or nonrespiratory illness. No associations were found between pollutant concentrations and any of the pulmonary function measures considered (FVC, FEV1, FEV0.75, and MMEF). Children with a history of wheeze or asthma had a much higher prevalence of respiratory symptoms, and there was some evidence that the association between air pollutant concentrations and symptom rates was stronger among children with these markers for hyperreactive airways. These data provide further evidence that rates of respiratory illnesses and symptoms are elevated among children living in cities with high particulate pollution. They also suggest that children with hyperreactive airways may be particularly susceptible to other respiratory symptoms when exposed to these pollutants.(ABSTRACT TRUNCATED AT 250 WORDS)

Effects of ambient sulfur oxides and suspended particles on respiratory health of preadolescent children.

Abstract: Reported here are the results from an ongoing study of outdoor air pollution and respiratory health of children living in six cities in the eastern and midwestern United States. The study enrolled 10,106 white preadolescent children between 1974 and 1977 in 3 successive annual visits to each city. Each child received a spirometric examination, and a parent completed a standard questionnaire. Of this cohort, 8,380 children were seen for a second examination 1 yr later. An air pollution monitoring program was begun in each community at about the time of the first examination. For this report, measurements of total suspended particulates (TSP), the sulfate fraction of TSP (TSO/sub 4/), and sulfur dioxide (SO2) concentrations at study-affiliated outdoor stations were combined with measurements at other public and private monitoring sites to create a record of TSP, TSO/sub 4/, and SO/sub 2/ concentrations in each of 9 air pollution regions during the 1-yr period preceding each examination and, for TSP, during each child's lifetime up to the time of testing. Across the 6 cities, frequency of cough was significantly associated with the average of 24-h mean concentrations of all 3 air pollutants during the year preceding the health examination (p less thanmore » 0.01). Rates of bronchitis and a composite measure of lower respiratory illness were significantly associated with average particulate concentrations (p less than 0.05). In analyses restricted to lifetime residents, these outcomes were significantly associated with measures of lifetime mean TSP concentration. Within the cities, however, temporal and spatial variation in air pollutant concentrations and illness and symptom rates were not positively associated.« less

Change in pulmonary function in children associated with air pollution episodes.

Abstract: Pulmonary function of approximately 200 school children in Steubenville, OH was measured before and immediately following air pollution alerts in the fall of 1978 and 1979. TSP concentrations exceeded the National Primary Ambient Air Quality 24 h standards in 1978. SO2 exceeded the standard in 1979. The children were then reexamined in three weekly visits following each alert. Estimated mean Forced Vital Capacity (FVC) was approximately 2% lower following each alert, although the lowest means were observed one to two weeks after the episodes. Forced Expired Volume in 0.75 sec (FEV0.75) did not change during the 1978 study, but was 4% lower immediately following the 1979 alert. The children were measured again in five weekly examinations in the spring and fall of 1980. Air pollution levels did not exceed the standards on either occasion. In the spring of 1980, estimated mean FVC and FEV0.75 showed a decline similar to that observed following the alerts in 1978 and 1979. In the fall of 1980, there were no s...

Fitting Linear Mixed-Effects Models Using lme4

Abstract: Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer.

Longitudinal data analysis using generalized linear models

Abstract: SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating equations are derived without specifying the joint distribution of a subject's observations yet they reduce to the score equations for multivariate Gaussian outcomes. Asymptotic theory is presented for the general class of estimators. Specific cases in which we assume independence, m-dependence and exchangeable correlation structures from each subject are discussed. Efficiency of the proposed estimators in two simple situations is considered. The approach is closely related to quasi-likelih ood. Some key ironh: Estimating equation; Generalized linear model; Longitudinal data; Quasi-likelihood; Repeated measures.

Bayesian measures of model complexity and fit

Abstract: Summary. We consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined. Using an information theoretic argument we derive a measure pD for the effective number of parameters in a model as the difference between the posterior mean of the deviance and the deviance at the posterior means of the parameters of interest. In general pD approximately corresponds to the trace of the product of Fisher's information and the posterior covariance, which in normal models is the trace of the ‘hat’ matrix projecting observations onto fitted values. Its properties in exponential families are explored. The posterior mean deviance is suggested as a Bayesian measure of fit or adequacy, and the contributions of individual observations to the fit and complexity can give rise to a diagnostic plot of deviance residuals against leverages. Adding pD to the posterior mean deviance gives a deviance information criterion for comparing models, which is related to other information criteria and has an approximate decision theoretic justification. The procedure is illustrated in some examples, and comparisons are drawn with alternative Bayesian and classical proposals. Throughout it is emphasized that the quantities required are trivial to compute in a Markov chain Monte Carlo analysis.

Missing data: Our view of the state of the art.

Abstract: Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.

Experimental Design and Data Analysis for Biologists

Abstract: An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software