Imperial College London

ProfessorSylviaRichardson

Faculty of MedicineSchool of Public Health

Visiting Professor
 
 
 
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Contact

 

+44 (0)20 7594 3336sylvia.richardson Website

 
 
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Assistant

 

Miss Sonia Kharbotli +44 (0)20 7594 3319

 
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Location

 

161Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Publication Type
Year
to

153 results found

Newcombe PJ, Ali HR, Blows FM, Provenzano E, Pharoah PD, Caldas C, Richardson Set al., 2017, Weibull regression with Bayesian variable selection to identify prognostic tumour markers of breast cancer survival, STATISTICAL METHODS IN MEDICAL RESEARCH, Vol: 26, Pages: 414-436, ISSN: 0962-2802

JOURNAL ARTICLE

Greene D, Richardson S, Turro E, 2016, Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases, AMERICAN JOURNAL OF HUMAN GENETICS, Vol: 98, Pages: 490-499, ISSN: 0002-9297

JOURNAL ARTICLE

Lewin A, Saadi H, Peters JE, Moreno-Moral A, Lee JC, Smith KGC, Petretto E, Bottolo L, Richardson Set al., 2016, MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues, BIOINFORMATICS, Vol: 32, Pages: 523-532, ISSN: 1367-4803

JOURNAL ARTICLE

Mattei F, Liverani S, Guida F, Matrat M, Cenee S, Azizi L, Menvielle G, Sanchez M, Pilorget C, Lapotre-Ledoux B, Luce D, Richardson S, Stucker Iet al., 2016, Multidimensional analysis of the effect of occupational exposure to organic solvents on lung cancer risk: the ICARE study, OCCUPATIONAL AND ENVIRONMENTAL MEDICINE, Vol: 73, Pages: 368-377, ISSN: 1351-0711

JOURNAL ARTICLE

Papathomas M, Richardson S, 2016, Exploring dependence between categorical variables: Benefits and limitations of using variable selection within Bayesian clustering in relation to log-linear modelling with interaction terms, JOURNAL OF STATISTICAL PLANNING AND INFERENCE, Vol: 173, Pages: 47-63, ISSN: 0378-3758

JOURNAL ARTICLE

Geneletti S, O'Keeffe AG, Sharples LD, Richardson S, Baio Get al., 2015, Bayesian regression discontinuity designs: incorporating clinical knowledge in the causal analysis of primary care data, STATISTICS IN MEDICINE, Vol: 34, Pages: 2334-2352, ISSN: 0277-6715

JOURNAL ARTICLE

Hastie DI, Liverani S, Richardson S, 2015, Sampling from Dirichlet process mixture models with unknown concentration parameter: mixing issues in large data implementations, STATISTICS AND COMPUTING, Vol: 25, Pages: 1023-1037, ISSN: 0960-3174

JOURNAL ARTICLE

Liverani S, Hastie DI, Azizi L, Papathomas M, Richardson Set al., 2015, PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes, JOURNAL OF STATISTICAL SOFTWARE, Vol: 64, Pages: 1-30, ISSN: 1548-7660

JOURNAL ARTICLE

Vallejos CA, Marioni JC, Richardson S, 2015, BASiCS: Bayesian Analysis of Single-Cell Sequencing Data, PLOS COMPUTATIONAL BIOLOGY, Vol: 11, ISSN: 1553-734X

JOURNAL ARTICLE

Wallace C, Cutler AJ, Pontikos N, Pekalski ML, Burren OS, Cooper JD, Garcia AR, Ferreira RC, Guo H, Walker NM, Smyth DJ, Rich SS, Onengut-Gumuscu S, Sawcer SJ, Ban M, Richardson S, Todd JA, Wicker LSet al., 2015, Dissection of a Complex Disease Susceptibility Region Using a Bayesian Stochastic Search Approach to Fine Mapping, PLOS GENETICS, Vol: 11, ISSN: 1553-7404

JOURNAL ARTICLE

Chadeau-Hyam M, Tubert-Bitter P, Guihenneuc-Jouyaux C, Campanella G, Richardson S, Vermeulen R, De Iorio M, Galea S, Vineis Pet al., 2014, Dynamics of the Risk of Smoking-Induced Lung Cancer A Compartmental Hidden Markov Model for Longitudinal Analysis, EPIDEMIOLOGY, Vol: 25, Pages: 28-34, ISSN: 1044-3983

JOURNAL ARTICLE

Chen L, Kostadima M, Martens JHA, Canu G, Garcia SP, Turro E, Downes K, Macaulay IC, Bielczyk-Maczynska E, Coe S, Farrow S, Poudel P, Burden F, Jansen SBG, Astle WJ, Attwood A, Bariana T, de Bono B, Breschi A, Chambers JC, Choudry FA, Clarke L, Coupland P, van der Ent M, Erber WN, Jansen JH, Favier R, Fenech ME, Foad N, Freson K, van Geet C, Gomez K, Guigo R, Hampshire D, Kelly AM, Kerstens HHD, Kooner JS, Laffan M, Lentaigne C, Labalette C, Martin T, Meacham S, Mumford A, Nuernberg S, Palumbo E, van der Reijden BA, Richardson D, Sammut SJ, Slodkowicz G, Tamuri AU, Vasquez L, Voss K, Watt S, Westbury S, Flicek P, Loos R, Goldman N, Bertone P, Read RJ, Richardson S, Cvejic A, Soranzo N, Ouwehand WH, Stunnenberg HG, Frontini M, Rendon Aet al., 2014, Transcriptional diversity during lineage commitment of human blood progenitors, SCIENCE, Vol: 345, Pages: 1580-+, ISSN: 0036-8075

JOURNAL ARTICLE

Li G, Haining R, Richardson S, Best Net al., 2014, Space-time variability in burglary risk: A Bayesian spatio-temporal modelling approach, SPATIAL STATISTICS, Vol: 9, Pages: 180-191, ISSN: 2211-6753

JOURNAL ARTICLE

Molitor J, Brown IJ, Chan Q, Papathomas M, Liverani S, Molitor N, Richardson S, Van Horn L, Daviglus ML, Dyer A, Stamler J, Elliott Pet al., 2014, Blood Pressure Differences Associated With Optimal Macronutrient Intake Trial for Heart Health (OMNIHEART)-Like Diet Compared With a Typical American Diet, HYPERTENSION, Vol: 64, Pages: 1198-U86, ISSN: 0194-911X

JOURNAL ARTICLE

Pettit J-B, Tomer R, Achim K, Richardson S, Azizi L, Marioni Jet al., 2014, Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets, PLOS COMPUTATIONAL BIOLOGY, Vol: 10, ISSN: 1553-734X

JOURNAL ARTICLE

Zucknick M, Richardson S, 2014, MCMC algorithms for Bayesian variable selection in the logistic regression model for large-scale genomic applications

In large-scale genomic applications vast numbers of molecular features arescanned in order to find a small number of candidates which are linked to aparticular disease or phenotype. This is a variable selection problem in the"large p, small n" paradigm where many more variables than samples areavailable. Additionally, a complex dependence structure is often observed amongthe markers/genes due to their joint involvement in biological processes andpathways. Bayesian variable selection methods that introduce sparseness throughadditional priors on the model size are well suited to the problem. However,the model space is very large and standard Markov chain Monte Carlo (MCMC)algorithms such as a Gibbs sampler sweeping over all p variables in eachiteration are often computationally infeasible. We propose to employ thedependence structure in the data to decide which variables should always beupdated together and which are nearly conditionally independent and hence donot need to be considered together. Here, we focus on binary classificationapplications. We follow the implementation of the Bayesian probit regressionmodel by Albert and Chib (1993) and the Bayesian logistic regression model byHolmes and Held (2006) which both lead to marginal Gaussian distributions. Wein- vestigate several MCMC samplers using the dependence structure in differentways. The mixing and convergence performances of the resulting Markov chainsare evaluated and compared to standard samplers in two simulation studies andin an application to a real gene expression data set.

JOURNAL ARTICLE

Bottolo L, Chadeau-Hyam M, Hastie DI, Zeller T, Liquet B, Newcombe P, Yengo L, Wild PS, Schillert A, Ziegler A, Nielsen SF, Butterworth AS, Ho WK, Castagne R, Munzel T, Tregouet D, Falchi M, Cambien F, Nordestgaard BG, Fumeron F, Tybjaerg-Hansen A, Froguel P, Danesh J, Petretto E, Blankenberg S, Tiret L, Richardson Set al., 2013, GUESS-ing Polygenic Associations with Multiple Phenotypes Using a GPU-Based Evolutionary Stochastic Search Algorithm, PLOS GENETICS, Vol: 9, ISSN: 1553-7404

JOURNAL ARTICLE

Geneletti S, Best N, Toledano MB, Elliott P, Richardson Set al., 2013, Uncovering selection bias in case-control studies using Bayesian post-stratification, STATISTICS IN MEDICINE, Vol: 32, Pages: 2555-2570, ISSN: 0277-6715

JOURNAL ARTICLE

Hansell AL, Blangiardo M, Fortunato L, Floud S, de Hoogh K, Fecht D, Ghosh RE, Laszlo HE, Pearson C, Beale L, Beevers S, Gulliver J, Best N, Richardson S, Elliott Pet al., 2013, Aircraft noise and cardiovascular disease near Heathrow airport in London: small area study, BMJ-BRITISH MEDICAL JOURNAL, Vol: 347, ISSN: 1756-1833

JOURNAL ARTICLE

Hastie DI, Liverani S, Azizi L, Richardson S, Stuecker Iet al., 2013, A semi-parametric approach to estimate risk functions associated with multi-dimensional exposure profiles: application to smoking and lung cancer, BMC MEDICAL RESEARCH METHODOLOGY, Vol: 13, ISSN: 1471-2288

JOURNAL ARTICLE

Kirk P, Witkover A, Bangham CRM, Richardson S, Lewin AM, Stumpf MPHet al., 2013, Balancing the Robustness and Predictive Performance of Biomarkers, JOURNAL OF COMPUTATIONAL BIOLOGY, Vol: 20, Pages: 979-989, ISSN: 1066-5277

JOURNAL ARTICLE

Li G, Haining R, Richardson S, Best Net al., 2013, Evaluating the No Cold Calling zones in Peterborough, England: application of a novel statistical method for evaluating neighbourhood policing policies, ENVIRONMENT AND PLANNING A, Vol: 45, Pages: 2012-2026, ISSN: 0308-518X

JOURNAL ARTICLE

Ancelet S, Abellan JJ, Vilas VJDR, Birch C, Richardson Set al., 2012, Bayesian shared spatial-component models to combine and borrow strength across sparse disease surveillance sources, BIOMETRICAL JOURNAL, Vol: 54, Pages: 385-404, ISSN: 0323-3847

JOURNAL ARTICLE

Astle W, De Iorio M, Richardson S, Stephens D, Ebbels Tet al., 2012, A Bayesian Model of NMR Spectra for the Deconvolution and Quantification of Metabolites in Complex Biological Mixtures, JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol: 107, Pages: 1259-1271, ISSN: 0162-1459

JOURNAL ARTICLE

Bergersen LC, Ahmed I, Frigessi A, Glad IK, Richardson Set al., 2012, Safe preselection in lasso-type problems by cross-validation freezing

We propose a new approach to safe variable preselection in high-dimensionalpenalized regression, such as the lasso. Preselection - to start with amanageable set of covariates - has often been implemented without clearappreciation of its potential bias. Based on sequential implementation of thelasso with increasing lists of predictors, we find a new property of the set ofcorresponding cross-validation curves, a pattern that we call freezing. Itallows to determine a subset of covariates with which we reach the same lassosolution as would be obtained using the full set of covariates. Freezing hasnot been characterized before and is different from recently discussed saferules for discarding predictors. We demonstrate by simulation that rankingpredictors by their univariate correlation with the outcome, leads in amajority of cases to early freezing, giving a safe and efficient way offocusing the lasso analysis on a smaller and manageable number of predictors.We illustrate the applicability of our strategy in the context of a GWASanalysis and on microarray genomic data. Freezing offers great potential forextending the applicability of penalized regressions to ultra highdimensionaldata sets. Its applicability is not limited to the standard lasso but is ageneric property of many penalized approaches.

JOURNAL ARTICLE

Clark SJ, Falchi M, Olsson B, Jacobson P, Cauchi S, Balkau B, Marre M, Lantieri O, Andersson JC, Jernas M, Aitman TJ, Richardson S, Sjostrom L, Wong HY, Carlsson LMS, Froguel P, Walley AJet al., 2012, Association of Sirtuin 1 (SIRT1) Gene SNPs and Transcript Expression Levels With Severe Obesity, OBESITY, Vol: 20, Pages: 178-185, ISSN: 1930-7381

JOURNAL ARTICLE

Ginestet CE, Best NG, Richardson S, 2012, Classification loss function for parameter ensembles in Bayesian hierarchical models, STATISTICS & PROBABILITY LETTERS, Vol: 82, Pages: 859-863, ISSN: 0167-7152

JOURNAL ARTICLE

Li G, Best N, Hansell AL, Ahmed I, Richardson Set al., 2012, BaySTDetect: detecting unusual temporal patterns in small area data via Bayesian model choice, BIOSTATISTICS, Vol: 13, Pages: 695-710, ISSN: 1465-4644

JOURNAL ARTICLE

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, ISSN: 1931-6690

JOURNAL ARTICLE

Mason A, Richardson S, Plewis I, Best Net al., 2012, Strategy for Modelling Nonrandom Missing Data Mechanisms in Observational Studies Using Bayesian Methods, JOURNAL OF OFFICIAL STATISTICS, Vol: 28, Pages: 279-302, ISSN: 0282-423X

JOURNAL ARTICLE

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