Publications
150 results found
Best N, Richardson S, Thomson A, 2005, A comparison of Bayesian spatial models for disease mapping, STATISTICAL METHODS IN MEDICAL RESEARCH, Vol: 14, Pages: 35-59, ISSN: 0962-2802
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- Citations: 343
Bennett J, Little MP, Richardson S, 2004, Flexible dose-response models for Japanese atomic bomb survivor data: Bayesian estimation and prediction of cancer risk, RADIATION AND ENVIRONMENTAL BIOPHYSICS, Vol: 43, Pages: 233-245, ISSN: 0301-634X
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- Citations: 25
Hansell AL, Lam KA, Richardson S, et al., 2004, Medical event profiling of COPD patients, PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, Vol: 13, Pages: 547-555, ISSN: 1053-8569
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- Citations: 10
Richardson S, Thomson A, Best N, et al., 2004, Interpreting posterior relative risk estimates in disease-mapping studies, ENVIRONMENTAL HEALTH PERSPECTIVES, Vol: 112, Pages: 1016-1025, ISSN: 0091-6765
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- Citations: 357
Best N, Chambers R, Jackson C, et al., 2004, Ecological inference for 2x2 tables - Discussion, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, Vol: 167, Pages: 426-445, ISSN: 0964-1998
Broët P, Lewin A, Richardson S, et al., 2004, A mixture model based strategy for selecting sets of genes in multiclass response microarray experiments., Bioinformatics
Seaman SR, Richardson S, 2004, Equivalence of prospective and retrospective models in the Bayesian analysis of case-control studies, Biometrika, Vol: 91, Pages: 15-25, ISSN: 0006-3444
Cressie N, Richardson S, Jaussent I, 2004, Ecological bias: use of maximum entropy approximations, Australian and New Zealand Journal of Statistics, Vol: 46
Richardson S, 2003, Highly Structured Stochastic Systems, Publisher: Oxford University Press
Knorr-Held L, Richardson S, 2003, A hierarchical model for space-time surveillance data on meningococcal disease incidence, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, Vol: 52, Pages: 169-183, ISSN: 0035-9254
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- Citations: 50
S Richardson, 2003, Spatial models in epidemiological applications, Highly Structured Stochastic Systems, Editors: Green, Hjort, Richardson, Publisher: Oxford University Press, Pages: 237-259
Richardson S, Best N, 2003, Bayesian hierarchical models in ecological studies of health environment effects, Environmetrics, Vol: 14, Pages: 129-147
Green PJ, Richardson S, 2002, Hidden Markov models and disease mapping, JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol: 97, Pages: 1055-1070, ISSN: 0162-1459
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- Citations: 214
Broët P, Richardson S, Radvanyi F, 2002, Bayesian hierarchical model for identifying changes in gene expression from microarray experiments, JOURNAL OF COMPUTATIONAL BIOLOGY, Vol: 9, Pages: 671-683, ISSN: 1066-5277
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- Citations: 65
Viallefont V, Richardson S, Green PJ, 2002, Bayesian analysis of poisson mixtures, Workshop on Statistical Models and Methods for Discontinuous Phenomena, Publisher: TAYLOR & FRANCIS LTD, Pages: 181-202, ISSN: 1048-5252
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- Citations: 30
Seaman SR, Richardson S, Stücker I, et al., 2002, A Bayesian partition model for case-control studies on highly polymorphic candidate genes, GENETIC EPIDEMIOLOGY, Vol: 22, Pages: 356-368, ISSN: 0741-0395
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- Citations: 12
Richardson S, Leblond L, Jaussent I, et al., 2002, Mixture models in measurement error problems, with reference to epidemiological studies, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, Vol: 165, Pages: 549-566, ISSN: 0964-1998
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- Citations: 44
Balding DJ, Carothers AD, Marchini JL, et al., 2002, Discussion on the meeting on 'Statistical modelling and analysis of genetic data', JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, Vol: 64, Pages: 737-775, ISSN: 1369-7412
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- Citations: 13
Jarup L, Briggs D, de Hoogh C, et al., 2002, Cancer risks in populations living near landfill sites in Great Britain., British Journal of Cancer, Vol: 86, Pages: 1732-1736
Seaman SR, Richardson S, 2001, Bayesian analysis of case-control studies with categorical covariates, BIOMETRIKA, Vol: 88, Pages: 1073-1088, ISSN: 0006-3444
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- Citations: 27
Viallefont V, Raftery AE, Richardson S, 2001, Variable selection and Bayesian model averaging in case-control studies, STATISTICS IN MEDICINE, Vol: 20, Pages: 3215-3230, ISSN: 0277-6715
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- Citations: 193
Elliott P, Briggs D, Morris S, et al., 2001, Risk of adverse birth outcomes in populations living near landfill sites, BRITISH MEDICAL JOURNAL, Vol: 323, Pages: 363-368, ISSN: 0959-535X
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- Citations: 216
Green PJ, Richardson S, 2001, Modelling heterogeneity with and without the Dirichlet process, Scand J Stat, Vol: 28, Pages: 355-375, ISSN: 0303-6898
Little MP, Deltour I, Richardson S, 2000, Projection of cancer risks from the Japanese atomic bomb survivors to the England and Wales population taking into account uncertainty in risk parameters., Radiat Environ Biophys, Vol: 39, Pages: 241-252, ISSN: 0301-634X
Generalized relative risk models, with adjustments to the relative risk for time after exposure and age at exposure and incorporating a linear-quadratic dose response, were fitted to the latest (Life Span Study Report 12) Japanese atomic bomb survivor cancer mortality data using Bayesian Markov Chain Monte Carlo methods, taking account of random errors in the DS86 dose estimates. The resulting uncertainty distributions in the relative risk model parameters were used to derive uncertainties in population cancer risks for a current UK population. Following an assumed administered dose of 1 Sv, leukaemia mortality risks were estimated to be 1.93x10(-2) Sv(-1) (95% CI 1.14, 3.38), or 0.44 years of life lost Sv(-1) (95% CI 0.22, 0.94). Following an assumed administered dose of 1 Sv, solid cancer mortality risks were calculated to be 10.36x10(-2) Sv(-1) (95% CI 8.41, 12.42), or 1.38 years of life lost Sv(-1) (95% CI 1.11, 1.68). In general, solid cancer risks were very similar to those predicted by classical likelihood-based methods; however, leukaemia risks were somewhat higher, by 10-35%, than those predicted by classical likelihood-based methods. This is so in both cases, irrespective of whether or not adjustments are made in these likelihood-based fits for the effects of measurement errors, and the discrepancy for leukaemia tends to be greater at higher doses. Overall, cancer risks predicted by Bayesian Markov Chain Monte Carlo methods are similar to those derived by classical likelihood-based methods and which form the basis of established estimates of radiation-induced cancer risk.
Cordier S, Monfort C, Miossec L, et al., 2000, Ecological analysis of digestive cancer mortality related to contamination by diarrhetic shellfish poisoning toxins along the coasts of France., Environ Res, Vol: 84, Pages: 145-150, ISSN: 0013-9351
Shellfish consumers are exposed to the risk of diarrhea from, among other contaminants, algae that produce diarrhetic shellfish poisoning (DSP) toxins, such as Dinophysis spp. These illnesses have been effectively prevented since 1984, when a phycotoxin monitoring network was set up along the coasts of France. There is nonetheless concern that residual levels of okadaic acid, a known tumor promoter that is the main toxin present in French coastal waters, might increase the risk of cancer among regular shellfish consumers. To test this hypothesis, we conducted an ecological study linking digestive cancer mortality rates with a proxy measure of contamination by DSP toxins in 59 coastal areas. Observed and expected numbers of deaths (using national rates as the reference) were computed by sex, cause of death, and area for two time periods: 1984-1988 and 1989-1993. The level of contamination in each area was estimated by the total number of weeks since monitoring began that production was shut down because of DSP toxin contamination. Using both Poisson regressions and test for trends of standardized mortality ratios across four exposure categories, we found some evidence of associations for several digestive cancer sites (esophagus, stomach, colon, liver, and total digestive cancers for men; stomach and pancreatic cancers for women). Among men, the only statistically significant result that remained after taking possible confounding by alcohol use into account involved colon cancer. The conclusions provided by this analysis are very tentative; they need to be reproduced and interpreted in the light of additional information on the potential long-term effects of DSP toxins. In the absence of human data, they provide some indication of a possible association between exposure to DSP toxins and digestive cancers.
Pascutto C, Wakefield JC, Best NG, et al., 2000, Statistical issues in the analysis of disease mapping data, STATISTICS IN MEDICINE, Vol: 19, Pages: 2493-2519, ISSN: 0277-6715
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- Citations: 92
Guihenneuc-Jouyaux C, Richardson S, Longini IM, 2000, Modeling markers of disease progression by a hidden Markov process: Application to characterizing CD4 cell decline, BIOMETRICS, Vol: 56, Pages: 733-741, ISSN: 0006-341X
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- Citations: 52
Richardson S, 2000, Methodological problems in ecological studies of health-environment effects., COMPTES RENDUS DE L ACADEMIE DES SCIENCES SERIE III-SCIENCES DE LA VIE-LIFE SCIENCES, Vol: 323, Pages: 611-616, ISSN: 0764-4469
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- Citations: 5
Richardson S, 2000, [Methodological problems in health-environment ecological studies]., C R Acad Sci III, Vol: 323, Pages: 611-616, ISSN: 0764-4469
Different approaches are used to study the effects of the environment on health. We restrict our discussion to observational studies at the aggregated level, the so-called ecological studies. We discuss several sources of bias for group-level studies and consider questions relating to the link between individual-level and group-level dose-effect relationship, the difference between group exposure and environmental exposure, and the influence of measurement error and variability in the exposure. Taking into consideration confounding factors in the analyses is another important item that is discussed. A final item concerns the necessity of studying the temporal direction of the effect, as well as assessing the existence of a potential threshold in the effect. As a broad conclusion, we can say that realistic quantification of uncertainty in dose-effect relationships is a delicate task that requires the systematic consideration of all sources of variability, as well as a transparent sensitivity analysis of the choices and hypotheses made during the statistical analysis.
Cockings S, Lars J, Aylin P, et al., 2000, A European Health and Environment Information System for disease and exposure mapping and risk assessment, EPIDEMIOLOGY, Vol: 11, Pages: S107-S107, ISSN: 1044-3983
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