Publications
150 results found
Mason A, Richardson S, Best N, 2012, Two-pronged strategy for using DIC to compare selection models with non-ignorable missing responses, Bayesian Analysis, Vol: 7, Pages: 109-146
Data with missing responses generated by a non-ignorable missing-ness mechanism can be analysed by jointly modelling the response and a binaryvariable indicating whether the response is observed or missing. Using a selectionmodel factorisation, the resulting joint model consists of a model of interest anda model of missingness. In the case of non-ignorable missingness, model choice isdi±cult because the assumptions about the missingness model are never veri¯ablefrom the data at hand. For complete data, the Deviance Information Criterion(DIC) is routinely used for Bayesian model comparison. However, when an anal-ysis includes missing data, DIC can be constructed in di®erent ways and its useand interpretation are not straightforward. In this paper, we present a strategy forcomparing selection models by combining information from two measures takenfrom di®erent constructions of the DIC. A DIC based on the observed data likeli-hood is used to compare joint models with di®erent models of interest but the samemodel of missingness, and a comparison of models with the same model of interestbut di®erent models of missingness is carried out using the model of missingnesspart of a conditional DIC. This strategy is intended for use within a sensitivityanalysis that explores the impact of di®erent assumptions about the two parts ofthe model, and is illustrated by examples with simulated missingness and an appli-cation which compares three treatments for depression using data from a clinicaltrial. We also examine issues relating to the calculation of the DIC based on theobserved data likelihood.
Ginestet C, Best N, Richardson S, 2012, Classification loss function for parameter ensembles in Bayesian hierarchical models, Statistics and Probability Letters, Vol: 82, Pages: 859-863
In this note, we consider the problem of classifying the elements of a parameter ensemble from a Bayesian hierarchical model as above or below a given threshold, C. Two threshold classification losses (TCLs)–termed balanced TCL and p-weighted TCL, respectively–are formulated. The p-weighted TCL can be used to prioritize the minimization of false positives over false negatives or the converse. We prove that, as a special case of a more general result, the p-weighted and balanced TCLs are optimized by the ensembles of unit-specific posterior (1−p)-quantiles and posterior medians, respectively. In addition, we also relate these classification loss functions on parameter ensembles to the concepts of posterior sensitivity and specificity. Finally, we discuss the potential applications of balanced and p-weighted TCLs in Bayesian hierarchical models, and how TCLs could be used to extend existing loss functions currently used for point estimation in parameter ensembles.
Bottolo L, Petretto E, Blankenberg S, et al., 2011, Bayesian Detection of Expression Quantitative Trait Loci Hot Spots, GENETICS, Vol: 189, Pages: 1449-+, ISSN: 0016-6731
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- Citations: 34
Kirk PDW, Witkover A, Courtney A, et al., 2011, Plasma proteome analysis in HTLV-1-associated myelopathy/tropical spastic paraparesis, Retrovirology, Vol: 8, ISSN: 1742-4690
Background: Human T lymphotropic virus Type 1 (HTLV-1) causes a chronic inflammatory disease of the central nervous system known as HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM) which resembles chronic spinal forms of multiple sclerosis (MS). The pathogenesis of HAM remains uncertain. To aid in the differential diagnosis of HAM and to identify pathogenetic mechanisms we analysed the plasma proteome in asymptomatic HTLV-1 carriers (ACs), patients with HAM, uninfected controls and patients with MS. We used surface-enhanced laser desorption-ionization (SELDI) mass spectrometry to analyse the plasma proteome in 68 HTLV-1-infected individuals (in two non-overlapping sets, each comprising 17 patients with HAM and 17 ACs), 16 uninfected controls, and 11 patients with secondary progressive MS. Candidate biomarkers were identified by tandem Q-TOF mass spectrometry. Results: The concentrations of three plasma proteins – high [2-microglobulin], high [Calgranulin B], and low [apolipoprotein A2] – were specifically associated with HAM, independently of proviral load. The plasma [2-microglobulin] was positively correlated with disease severity. Conclusions: The results indicate that monocytes are activated by contact with activated endothelium in HAM. Using 2-microglobulin and Calgranulin B alone we derive a diagnostic algorithm that correctly classified the disease status (presence or absence of HAM) in 81% of HTLV-1-infected subjects in the cohort.
Molitor J, Su JG, Molitor N-T, et al., 2011, Identifying vulnerable populations through an examination of the association between multipollutant profiles and poverty., Environ Sci Technol, Vol: 45, Pages: 7754-7760
Recently, concerns have centered on how to expand knowledge on the limited science related to the cumulative impact of multiple air pollution exposures and the potential vulnerability of poor communities to their toxic effects. The highly intercorrelated nature of exposures makes application of standard regression-based methods to these questions problematic due to well-known issues related to multicollinearity. Our paper addresses these problems by using, as its basic unit of inference, a profile consisting of a pattern of exposure values. These profiles are grouped into clusters and associated with a deprivation outcome. Specifically, we examine how profiles of NO(2)-, PM(2.5)-, and diesel- (road and off-road) based exposures are associated with the number of individuals living under poverty in census tracts (CT's) in Los Angeles County. Results indicate that higher levels of pollutants are generally associated with higher poverty counts, though the association is complex and nonlinear. Our approach is set in the Bayesian framework, and as such the entire model can be fit as a unit using modern Bayesian multilevel modeling techniques via the freely available WinBUGS software package, (1) though we have used custom-written C++ code (validated with WinBUGS) to improve computational speed. The modeling approach proposed thus goes beyond single-pollutant models in that it allows us to determine the association between entire multipollutant profiles of exposures with poverty levels in small geographic areas in Los Angeles County.
Walley AJ, Jacobson P, Falchi M, et al., 2011, Differential coexpression analysis of obesity-associated networks in human subcutaneous adipose tissue, International Journal of Obesity, Vol: 36, Pages: 137-147, ISSN: 0307-0565
Objective:To use a unique obesity-discordant sib-pair study design to combine differential expression analysis, expression quantitative trait loci (eQTLs) mapping and a coexpression regulatory network approach in subcutaneous human adipose tissue to identify genes relevant to the obese state.Study design:Genome-wide transcript expression in subcutaneous human adipose tissue was measured using Affymetrix U133 Plus 2.0 microarrays (Affymetrix, Santa Clara, CA, USA), and genome-wide genotyping data was obtained using an Applied Biosystems (Applied Biosystems; Life Technologies, Carlsbad, CA, USA) SNPlex linkage panel.Subjects:A total of 154 Swedish families ascertained through an obese proband (body mass index (BMI) >30 kg m−2) with a discordant sibling (BMI>10 kg m−2 less than proband).Results:Approximately one-third of the transcripts were differentially expressed between lean and obese siblings. The cellular adhesion molecules (CAMs) KEGG grouping contained the largest number of differentially expressed genes under cis-acting genetic control. By using a novel approach to contrast CAMs coexpression networks between lean and obese siblings, a subset of differentially regulated genes was identified, with the previously GWAS obesity-associated neuronal growth regulator 1 (NEGR1) as a central hub. Independent analysis using mouse data demonstrated that this finding of NEGR1 is conserved across species.Conclusion:Our data suggest that in addition to its reported role in the brain, NEGR1 is also expressed in subcutaneous adipose tissue and acts as a central ‘hub’ in an obesity-related transcript network.
Fortunato L, Jose Abellan J, Beale L, et al., 2011, Spatio-temporal patterns of bladder cancer incidence in Utah (1973-2004) and their association with the presence of toxic release inventory sites, INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS, Vol: 10, ISSN: 1476-072X
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- Citations: 14
Blangiardo M, Richardson S, Gulliver J, et al., 2011, A Bayesian analysis of the impact of air pollution episodes on cardio-respiratory hospital admissions in the Greater London area, STATISTICAL METHODS IN MEDICAL RESEARCH, Vol: 20, Pages: 69-80, ISSN: 0962-2802
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- Citations: 4
Bottolo L, Chadeau-Hyam M, Hastie DI, et al., 2011, ESS++: a C++ objected-oriented algorithm for Bayesian stochastic search model exploration, Bioinformatics, Vol: 27, Pages: 587-588, ISSN: 1367-4803
Summary:ESS++ is a C++ implementation of a fully Bayesian variable selection approach for single and multiple response linear regression. ESS++ works well both when the number of observations is larger than the number of predictors and in the ‘large p, small n’ case. In the current version, ESS++ can handle several hundred observations, thousands of predictors and a few responses simultaneously. The core engine of ESS++ for the selection of relevant predictors is based on Evolutionary Monte Carlo. Our implementation is open source, allowing community-based alterations and improvements.Availability: C++ source code and documentation including compilation instructions are available under GNU licence at http://bgx.org.uk/software/ESS.html.
Papathomas M, Molitor J, Richardson S, et al., 2011, Examining the Joint Effect of Multiple Risk Factors Using Exposure Risk Profiles: Lung Cancer in Nonsmokers, ENVIRONMENTAL HEALTH PERSPECTIVES, Vol: 119, Pages: 84-91, ISSN: 0091-6765
Background: Profile regression is a Bayesian statistical approach designed for investigating thejoint effect of multiple risk factors. It reduces dimensionality by using as its main unit of inferencethe exposure profiles of the subjects that is, the sequence of covariate values that correspond toeach subject.Objectives: We applied profile regression to a case–control study of lung cancer in nonsmokers,nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort,to estimate the combined effect of environmental carcinogens and to explore possible gene–environment interactions.Methods: We tailored and extended the profile regression approach to the analysis of case–controlstudies, allowing for the analysis of ordinal data and the computation of posterior odds ratios. Wecompared and contrasted our results with those obtained using standard logistic regression and classificationtree methods, including multifactor dimensionality reduction.Results: Profile regression strengthened previous observations in other study populations on therole of air pollutants, particularly particulate matter ≤ 10 μm in aerodynamic diameter (PM10), inlung cancer for nonsmokers. Covariates including living on a main road, exposure to PM10 andnitrogen dioxide, and carrying out manual work characterized high-risk subject profiles. Such combinationsof risk factors were consistent with a priori expectations. In contrast, other methods gaveless interpretable results.Conclusions: We conclude that profile regression is a powerful tool for identifying risk profilesthat express the joint effect of etiologically relevant variables in multifactorial diseases.
Vilas VJDR, Ancelet S, Abellan JJ, et al., 2011, A Bayesian hierarchical analysis to compare classical and atypical scrapie surveillance data; Wales 2002-2006, PREVENTIVE VETERINARY MEDICINE, Vol: 98, Pages: 29-38, ISSN: 0167-5877
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- Citations: 2
Blangiardo M, Hansell A, Richardson S, 2011, A Bayesian model of time activity data to investigate health effect of air pollution in time series studies, ATMOSPHERIC ENVIRONMENT, Vol: 45, Pages: 379-386, ISSN: 1352-2310
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- Citations: 22
Ahmed I, Hartikainen A-L, Jarvelin M-R, et al., 2011, False Discovery Rate Estimation for Stability Selection: Application to Genome-Wide Association Studies, STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, Vol: 10, ISSN: 2194-6302
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- Citations: 8
Turro E, Su S-Y, Goncalves A, et al., 2011, Haplotype and isoform specific expression estimation using multi-mapping RNA-seq reads, GENOME BIOLOGY, Vol: 12, ISSN: 1474-760X
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- Citations: 173
Brown AA, Richardson S, Whittaker J, 2011, Application of the Lasso to Expression Quantitative Trait Loci Mapping, STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, Vol: 10, ISSN: 2194-6302
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- Citations: 3
Gustafson P, McCandless LC, Levy AR, et al., 2010, Simplified Bayesian Sensitivity Analysis for Mismeasured and Unobserved Confounders, BIOMETRICS, Vol: 66, Pages: 1129-1137, ISSN: 0006-341X
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- Citations: 18
Haining R, Li GQ, Maheswaran R, et al., 2010, Inference from ecological models: estimating the relative risk of stroke from air pollution exposure using small area data, Spatial and Spatio-temporal Epidemiology, Vol: 1, Pages: 123-131
Blangiardo M, Cassese A, Richardson S, 2010, sdef: an R package to synthesize lists of significant features in related experiments, BMC BIOINFORMATICS, Vol: 11, ISSN: 1471-2105
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- Citations: 8
Petretto E, Bottolo L, Langley SR, et al., 2010, New Insights into the Genetic Control of Gene Expression using a Bayesian Multi-tissue Approach, PLOS COMPUTATIONAL BIOLOGY, Vol: 6, ISSN: 1553-734X
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- Citations: 52
Richardson JMMPMJAS, 2010, Bayesian profile regression with an application to the National survey of children's health, Vol: 11, Pages: 484-498
Paige AJW, Zucknick M, Janczar S, et al., 2010, <i>WWOX</i> tumour suppressor gene polymorphisms and ovarian cancer pathology and prognosis, EUROPEAN JOURNAL OF CANCER, Vol: 46, Pages: 818-825, ISSN: 0959-8049
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- Citations: 24
Ratmann O, Andrieu C, Wiuf C, et al., 2010, Reply to Robert et al.: Model criticism informs model choice and model comparison, Proceedings of the National Academy of Sciences of the United States of America, Vol: 107, Pages: E6-E7, ISSN: 0027-8424
In their letter to PNAS and a comprehensive set of notes on arXiv[arXiv:0909.5673v2], Christian Robert, Kerrie Mengersen and Carla Chen (RMC)represent our approach to model criticism in situations when the likelihoodcannot be computed as a way to "contrast several models with each other". Inaddition, RMC argue that model assessment with Approximate Bayesian Computationunder model uncertainty (ABCmu) is unduly challenging and question its Bayesianfoundations. We disagree, and clarify that ABCmu is a probabilistically soundand powerful too for criticizing a model against aspects of the observed data,and discuss further the utility of ABCmu.
Turro E, Lewin A, Rose A, et al., 2010, MMBGX: a method for estimating expression at the isoform level and detecting differential splicing using whole-transcript Affymetrix arrays, NUCLEIC ACIDS RESEARCH, Vol: 38, ISSN: 0305-1048
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- Citations: 18
Bottolo L, Richardson S, 2010, Evolutionary Stochastic Search for Bayesian Model Exploration, BAYESIAN ANALYSIS, Vol: 5, Pages: 583-618, ISSN: 1931-6690
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- Citations: 91
Richardson S, Guihenneuc-Jouyaux C, 2009, Impact of Cliff and Ord (1969,1981) on Spatial Epidemiology, GEOGRAPHICAL ANALYSIS, Vol: 41, Pages: 444-451, ISSN: 0016-7363
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- Citations: 2
Ratmann O, Andrieu C, Wiuf C, et al., 2009, Model criticism based on likelihood-free inference, with an application to protein network evolution, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 106, Pages: 10576-10581, ISSN: 0027-8424
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- Citations: 107
Goria S, Daniau C, de Crouy-Chanel P, et al., 2009, Risk of cancer in the vicinity of municipal solid waste incinerators: importance of using a flexible modelling strategy, INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS, Vol: 8, ISSN: 1476-072X
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- Citations: 13
Jackson CH, Best NG, Richardson S, 2009, Bayesian graphical models for regression on multiple data sets with different variables, BIOSTATISTICS, Vol: 10, Pages: 335-351, ISSN: 1465-4644
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- Citations: 35
Elliott P, Richardson S, Abellan JJ, et al., 2009, Geographic density of landfill sites and risk of congenital anomalies in England, OCCUPATIONAL AND ENVIRONMENTAL MEDICINE, Vol: 66, Pages: 81-89, ISSN: 1351-0711
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- Citations: 40
Elliott P, Richardson S, Abellan JJ, et al., 2009, Geographic density of landfill sites and risk of congenital anomalies in England: authors' response, OCCUPATIONAL AND ENVIRONMENTAL MEDICINE, Vol: 66, Pages: 140-140, ISSN: 1351-0711
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- Citations: 2
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