Biomedical subjects

Jon Wakefield

Publications and source records attributed to Jon Wakefield.

11 recordsLinked to original sources

PubMed2006

The automated counting of spots for the ELISpot assay.

An automated method for counting spot-forming units in the ELISpot assay is described that uses a statistical model fit to training data that is based on counts from one or more experts. The method adapts to variable background intensities and provides considerable flexibility with respect to what image features can be used to model expert counts. Point estimates of spot counts are produced together with intervals that reflect the degree of uncertainty in the count. Finally, the approach is completely transparent and "open source" in contrast to methods embedded in current commercial software. An illustrative application to data from a study of the reactivity of T-cells from healthy human subjects to a pool of immunodominant peptides from CMV, EBV and flu is presented.

Algorithms↗

PubMed2006

Disease mapping and spatial regression with count data.

In this paper, we provide critical reviews of methods suggested for the analysis of aggregate count data in the context of disease mapping and spatial regression. We introduce a new method for picking prior distributions, and propose a number of refinements of previously used models. We also consider ecological bias, mutual standardization, and choice of both spatial model and prior specification. We analyze male lip cancer incidence data collected in Scotland over the period 1975-1980, and outline a number of problems with previous analyses of these data. In disease mapping studies, hierarchical models can provide robust estimation of area-level risk parameters, though care is required in the choice of covariate model, and it is important to assess the sensitivity of estimates to the spatial model chosen, and to the prior specifications on the variance parameters. Spatial ecological regression is a far more hazardous enterprise for two reasons. First, there is always the possibility of ecological bias, and this can only be alleviated by the inclusion of individual-level data. For the Scottish data, we show that the previously used mean model has limited interpretation from an individual perspective. Second, when residual spatial dependence is modeled, and if the exposure has spatial structure, then estimates of exposure association parameters will change when compared with those obtained from the independence across space model, and the data alone cannot choose the form and extent of spatial correlation that is appropriate.

Epidemiologic Methods↗

PubMed2006

Health-exposure modeling and the ecological fallacy.

Recently, there has been an increased interest in modeling the association between aggregate disease counts and environmental exposures measured, for example via air pollution monitors, at point locations. This paper has two aims: first, we develop a model for such data in order to avoid ecological bias; second, we illustrate that modeling the exposure surface and estimating exposures may lead to bias in estimation of health effects. Design issues are also briefly considered, in particular the loss of information in moving from individual to ecological data, and the at-risk populations to consider in relation to the pollution monitor locations. The approach is investigated initially through simulations, and is then applied to a study of the association between mortality in those over 65 in the year 2000 and the previous year's SO2, in London. We conclude that the use of the proposed model can provide valid inference, but the use of estimated exposures should be carried out with great caution.

Aged↗

PubMed2006

A Bayesian mixture model for partitioning gene expression data.

In recent years there has been great interest in making inference for gene expression data collected over time. In this article, we describe a Bayesian hierarchical mixture model for partitioning such data. While conventional approaches cluster the observed data, we assume a nonparametric, random walk model, and partition on the basis of the parameters of this model. The model is flexible and can be tuned to the specific context, respects the order of observations within each curve, acknowledges measurement error, and allows prior knowledge on parameters to be incorporated. The number of partitions may also be treated as unknown, and inferred from the data, in which case computation is carried out via a birth-death Markov chain Monte Carlo algorithm. We first examine the behavior of the model on simulated data, along with a comparison with more conventional approaches, and then analyze meiotic expression data collected over time on fission yeast genes.

Bayes Theorem↗

PubMed2005

Modelling exposure to disinfection by-products in drinking water for an epidemiological study of adverse birth outcomes.

We are conducting an epidemiological study on the association between disinfection by-product concentrations in drinking water and adverse birth outcomes in the UK, using trihalomethane (THM) concentrations over defined water zones as an exposure index. Here we construct statistical models using sparse routinely collected THMs measurements to obtain quarterly estimates of mean THM concentrations for each water zone. We modelled the THM measurements using a Bayesian hierarchical mixture model, taking into account heterogeneity in THM concentrations between water originating from different source types, quarterly variation in THM concentrations and uncertainty in the true value of undetected and rounded measurements. Quarterly estimates of mean THM concentrations plus estimates of the water source type (ground, lowland surface or upland surface) were obtained for each water zone. THM concentration estimates were typically highest from July to September (third quarter), and varied considerably between water sources. Our exposure estimates were categorized into 'low', 'medium' and 'high' THM classes. Our modelled quarterly exposure estimates were compared to a simple alternative: annual means of the raw data for each water zone. In all, 15-25% of exposure estimates were classified differently. The modelled THM estimates led to slightly stronger and more precise estimates of association with risk of still birth and low birth weight than did the raw annual means. We conclude that our modelling approach enabled us to provide robust quarterly estimates of ecological exposure to THMs in a situation where the raw data were too sparse to base exposure assessment on empirical summaries alone.

Adult↗

PubMed2004

Controlling for provider of treatment in the modelling of respiratory disease risk near cokeworks.

The improved quality of hospital admissions data makes them a valuable resource for researchers. However, we show that, when multiple providers of health care are considered, the provider (in this case hospital) may act like other confounders such as socio-economic status, and hence must be controlled for. Such control is not as straightforward as for conventional confounders, however, but we describe a method that is appropriate under certain assumptions. We also describe a number of other statistical issues, such as the modelling of spatial and non-spatial overdispersion, that arose during the use of hospital data in a study to investigate the possible adverse health effects of living in proximity to six cokeworks groups in England and Wales. The outcome data that we consider consist of hospital admissions for all respiratory disease in the under-5s. The ecological level of the analysis is the census-defined enumeration district, and the main (proxy) exposure measure utilised is the spatial location of the enumeration district population-weighted centroid in relation to the cokeworks. We focus on the Teesside cokeworks group, for which we also had sulphur dioxide measurements from dispersion modelling as an alternative exposure measure. The major local providers varied appreciably in their standardized admission ratios for respiratory disease, and when provider was controlled for, the size of the observed excess risk found close to the cokeworks was decreased, making control for the provider of health care vital. However, the presence of multiple pollution sources, in addition to the usual shortcomings of ecological studies, makes interpretation difficult.

Air Pollutants↗

PubMed2003

Trends in drug overdose deaths in England and Wales 1993-98: methadone does not kill more people than heroin.

AIMS: To test the hypothesis that methadone is responsible for a greater increase in overdose deaths than heroin, and causes proportionally more overdose deaths than heroin at weekends. DESIGN AND SETTING: Multivariate analysis of 3961 death certificates mentioning heroin, morphine and/or methadone held on the Office for National Statistics drug-related poisoning mortality database from 1993 to 1998 in England and Wales. MEASUREMENTS: Percentage increase in deaths by year by drug, odds ratio (OR) of dying at the weekend from methadone-related overdose compared to dying from heroin/morphine overdose. FINDINGS: From 1993 to 1998, annual opiate overdose deaths increased from 378 to 909. There was a 24.7% (95% confidence interval (CI) 22-28%) yearly increase in heroin deaths compared to 9.4% (95% CI 6-13%) for methadone only. This difference was significant (P < 0.001 by test of interaction) after adjustment for sex, age group, polydrug use, area of residence and underlying cause of death. The largest number of deaths occurred on Saturday (673). The OR of death from methadone overdose on Saturday and Sunday was 1.48 (95% CI 1.29-1.71) for methadone-only deaths compared to dying from heroin/morphine at the weekend after adjustment for other covariates, but the OR was not significant (1.09, 95% CI 0.95-1.25) if the weekend was defined as Friday and Saturday. CONCLUSIONS: There was no evidence that the threefold increase in deaths over time was due to methadone. There was equivocal support only for the hypothesis that there was an excess of deaths from methadone at weekends. Increased interventions to prevent overdose among injectors in England and Wales are long overdue.

Adolescent↗

PubMed2003

Sensitivity analyses for ecological regression.

In many ecological regression studies investigating associations between environmental exposures and health outcomes, the observed relative risks are in the range 1.0-2.0. The interpretation of such small relative risks is difficult due to a variety of biases--some of which are unique to ecological data, since they arise from within-area variability in exposures/confounders. The potential for residual spatial dependence, due to unmeasured confounders and/or data anomalies with spatial structure, must also be considered, though it often will be of secondary importance when compared to the likely effects of unmeasured confounding and within-area variability in exposures/confounders. Methods for addressing sensitivity to these issues are described, along with an approach for assessing the implications of spatial dependence. An ecological study of the association between myocardial infarction and magnesium is critically reevaluated to determine potential sources of bias. It is argued that the sophistication of the statistical analysis should not outweigh the quality of the data, and that finessing models for spatial dependence will often not be merited in the context of ecological regression.

Bias↗

PubMed2002

Geographical epidemiology of prostate cancer in Great Britain.

Prostate cancer incidence has increased during recent years, possibly linked to environmental exposures. Exposure to environmental carcinogens is unlikely to be evenly distributed geographically, which may give rise to variations in disease occurrence that is detectable in a spatial analysis. The aim of our study was to examine the spatial variation of prostate cancer in Great Britain at ages 45-64 years. Spatial variation was examined across electoral wards from 1975-1991. Poisson regression was used to examine regional, urbanisation and socioeconomic effects, while Bayesian mapping techniques were used to assess spatial variability. There was an indication of geographical differences in prostate cancer risk at a regional level, ranging from 0.83 (95% CI: 0.78-0.87) to 1.2 (95% CI: 1.1-1.3) across regions. There was significant heterogeneity in the risk across wards, although the range of relative risks was narrow. More detailed spatial analyses within 4 regions did not indicate any clear evidence of localised geographical clustering for prostate cancer. The absence of any marked geographical variability at a small-area scale argues against a geographically varying environmental factor operating strongly in the aetiology of prostate cancer.

Bayes Theorem↗

PubMed2002

Bayesian analysis of population PK/PD models: general concepts and software.

Markov chain Monte Carlo (MCMC) techniques have revolutionized the field of Bayesian statistics by enabling posterior inference for arbitrarily complex models. The now widely used WinBUGS software has, over the years, made the methodology accessible to a great many applied scientists, in all fields of research. Despite this, serious application of MCMC methods within the field of population PK/PD has been comparatively limited. We appreciate that for many applied pharmacokineticists the prospect of conducting a Bayesian analysis will require numerous alien concepts to be taken on board and it may be difficult to justify investing the time and effort required in order to understand them (especially since the approach is so computer-intensive). For this reason we provide here a thorough (but often informal) discussion of all aspects of Bayesian inference as they apply specifically to population PK/PD. We also acknowledge that while the WinBUGS software is general purpose, model specification for some types of problem, population PK/PD being a prime example, can be very difficult, to the extent that a specialized interface for describing the problem at hand is often a practical necessity. In the latter part of this paper we describe such an interface, namely PKBugs. A principal aim of the paper is to offer sufficient technical background, in an easy to follow format, that the reader may develop both the confidence and know-how to make appropriate use of the PKBugs/WinBUGS framework (or similar software) for their own data analysis needs, should they choose to adopt a Bayesian approach.

Bayes Theorem↗

PubMed2002

A hierarchical aggregate data model with spatially correlated disease rates.

The aggregate data study design (Prentice and Sheppard, 1995, Biometrika 82, 113-125) estimates individual-level exposure effects by regressing population-based disease rates on covariate data from survey samples in each population group. In this work, we further develop the aggregate data model to allow for residual spatial correlation among disease rates across populations. Geographical variation that is not explained by model predictors and has a spatial component often arises in studies of rare chronic diseases, such as breast cancer. We combine the aggregate and Bayesian disease-mapping models to provide an intuitive approach to the modeling of spatial effects while drawing correct inference regarding the exposure effect. Based on the results of simulation studies, we suggest guidelines for use of the proposed model.

Bayes Theorem↗