Jon Wakefield | University of Washington (original) (raw)
Papers by Jon Wakefield
Journal of The American Statistical Association, 1996
Wakefield: Bayesian Analysis of Population Pharmacokinetic Models 63 chain Monte Carlo algorithm ... more Wakefield: Bayesian Analysis of Population Pharmacokinetic Models 63 chain Monte Carlo algorithm which was used to implement the analysis. Section 4 contains a concluding discussion. 2. A GENERAL HIERARCHICAL MODEL Population pharmacokinetic models fit naturally into ...
Occupational and Environmental Medicine, 2000
British Medical Journal, 2001
Biometrics, 2000
Pharmacokinetic data consist of drug concentrations with associated known sampling times and are ... more Pharmacokinetic data consist of drug concentrations with associated known sampling times and are collected following the administration of known dosage regimens. Population pharmacokinetic data consist of such data on a number of individuals, possibly along with individual-specific characteristics. During drug development, a number of population pharmacokinetic studies are typically carried out and the combination of such studies is of great importance for characterizing the drug and, in particular, for the design of future studies. In this paper, we describe a model that may be used to combine population pharmacokinetic data. The model is illustrated using six phase I studies of the antiasthmatic drug fluticasone propionate. Our approach is Bayesian and computation is carried out using Markov chain Monte Carlo. We provide a number of simplifications to the model that may be made in order to ease simulation from the posterior distribution.
Journal of The Royal Statistical Society Series A-statistics in Society, 2001
Statistics in Medicine, 1999
The availability of geographically indexed health and population data, with advances in computing... more The availability of geographically indexed health and population data, with advances in computing, geographical information systems and statistical methodology, have opened the way for serious exploration of small area health statistics based on routine data. Such analyses may be used to address specific questions concerning health in relation to sources of pollution, to investigate clustering of disease or for hypothesis generation. We distinguish four types of analysis: disease mapping; geographic correlation studies; the assessment of risk in relation to a prespecified point or line source, and cluster detection and disease clustering. A general framework for the statistical analysis of small area studies will be considered. This framework assumes that populations at risk arise from inhomogeneous Poisson processes. Disease cases are then realizations of a thinned Poisson process where the risk of disease depends on the characteristics of the person, time and spatial location. Difficulties of analysis and interpretation due to data inaccuracies and aggregation will be addressed with particular reference to ecological bias and confounding. The use of errors-in-variables modelling in small area analyses will be discussed.
Addiction, 2003
Aims To test the hypothesis that methadone is responsible for a greater increase in overdose dea... more 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.
Biometrics, 2003
In many ecological regression studies investigating associations between environmental exposures ... more 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.
International Journal of Cancer, 2002
Prostate cancer incidence has increased during recent years, possibly linked to environmental exp... more 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. © 2001 Wiley-Liss, Inc.
Journal of The American Statistical Association, 1996
Wakefield: Bayesian Analysis of Population Pharmacokinetic Models 63 chain Monte Carlo algorithm ... more Wakefield: Bayesian Analysis of Population Pharmacokinetic Models 63 chain Monte Carlo algorithm which was used to implement the analysis. Section 4 contains a concluding discussion. 2. A GENERAL HIERARCHICAL MODEL Population pharmacokinetic models fit naturally into ...
Occupational and Environmental Medicine, 2000
British Medical Journal, 2001
Biometrics, 2000
Pharmacokinetic data consist of drug concentrations with associated known sampling times and are ... more Pharmacokinetic data consist of drug concentrations with associated known sampling times and are collected following the administration of known dosage regimens. Population pharmacokinetic data consist of such data on a number of individuals, possibly along with individual-specific characteristics. During drug development, a number of population pharmacokinetic studies are typically carried out and the combination of such studies is of great importance for characterizing the drug and, in particular, for the design of future studies. In this paper, we describe a model that may be used to combine population pharmacokinetic data. The model is illustrated using six phase I studies of the antiasthmatic drug fluticasone propionate. Our approach is Bayesian and computation is carried out using Markov chain Monte Carlo. We provide a number of simplifications to the model that may be made in order to ease simulation from the posterior distribution.
Journal of The Royal Statistical Society Series A-statistics in Society, 2001
Statistics in Medicine, 1999
The availability of geographically indexed health and population data, with advances in computing... more The availability of geographically indexed health and population data, with advances in computing, geographical information systems and statistical methodology, have opened the way for serious exploration of small area health statistics based on routine data. Such analyses may be used to address specific questions concerning health in relation to sources of pollution, to investigate clustering of disease or for hypothesis generation. We distinguish four types of analysis: disease mapping; geographic correlation studies; the assessment of risk in relation to a prespecified point or line source, and cluster detection and disease clustering. A general framework for the statistical analysis of small area studies will be considered. This framework assumes that populations at risk arise from inhomogeneous Poisson processes. Disease cases are then realizations of a thinned Poisson process where the risk of disease depends on the characteristics of the person, time and spatial location. Difficulties of analysis and interpretation due to data inaccuracies and aggregation will be addressed with particular reference to ecological bias and confounding. The use of errors-in-variables modelling in small area analyses will be discussed.
Addiction, 2003
Aims To test the hypothesis that methadone is responsible for a greater increase in overdose dea... more 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.
Biometrics, 2003
In many ecological regression studies investigating associations between environmental exposures ... more 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.
International Journal of Cancer, 2002
Prostate cancer incidence has increased during recent years, possibly linked to environmental exp... more 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. © 2001 Wiley-Liss, Inc.