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A descriptive and modelbased spatial comparison of the standardised mortality ratio and the agestandardised mortality rate
, 2007
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Defining and characterising structural uncertainty in decision analytic models. Research Paper 9
, 2006
"... CHE Discussion Papers (DPs) began publication in 1983 as a means of making current research material more widely available to health economists and other potential users. So as to speed up the dissemination process, papers were originally published by CHE and distributed by post to a worldwide reade ..."
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CHE Discussion Papers (DPs) began publication in 1983 as a means of making current research material more widely available to health economists and other potential users. So as to speed up the dissemination process, papers were originally published by CHE and distributed by post to a worldwide readership. The new CHE Research Paper series takes over that function and provides access to current research output via webbased publication, although hard copy will continue to be available (but subject to charge). Disclaimer Papers published in the CHE Research Paper (RP) series are intended as a contribution to current research. Work and ideas reported in RPs may not always represent the final position and as such may sometimes need to be treated as work in progress. The material and views expressed in RPs are solely those of the authors and should not be interpreted as representing the collective views of CHE research staff or their research funders. Further copies Copies of this paper are freely available to download from the CHE website www.york.ac.uk/inst/che/pubs. Access to downloaded material is provided on the understanding that it is intended for personal use. Copies of downloaded papers may be distributed to thirdparties subject to the proviso that the CHE publication source is properly acknowledged and that such distribution is not subject to any payment. Printed copies are available on request at a charge of £5.00 per copy. Please contact the
Identifying interacting SNPs using Monte Carlo logic regression. Genetic Epidemiology, 28(2):157–170
, 2005
"... Interactions are frequently at the center of interest in singlenucleotide polymorphism (SNP) association studies. When interacting SNPs are in the same gene or in genes that are close in sequence, such interactions may suggest which haplotypes are associated with a disease. Interactions between unr ..."
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Interactions are frequently at the center of interest in singlenucleotide polymorphism (SNP) association studies. When interacting SNPs are in the same gene or in genes that are close in sequence, such interactions may suggest which haplotypes are associated with a disease. Interactions between unrelated SNPs may suggest genetic pathways. Unfortunately, data sets are often still too small to definitively determine whether interactions between SNPs occur. Also, competing sets of interactions could often be of equal interest. Here we propose Monte Carlo logic regression, an exploratory tool that combines Markov chain Monte Carlo and logic regression, an adaptive regression methodology that attempts to construct predictors as Boolean combinations of binary covariates such as SNPs. The goal of Monte Carlo logic regression is to generate a collection of (interactions of) SNPs that may be associated with a disease outcome, and that warrant further investigation. As such, the models that are fitted in the Markov chain are not combined into a single model, as is often done in Bayesian model averaging procedures. Instead, the most frequently occurring patterns in these models are tabulated. The method is applied to a study of heart disease with 779 participants and 89 SNPs. A simulation study is carried out to investigate the performance of the Monte Carlo logic regression approach. Genet. Epidemiol. 28:157–170, 2005.
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"... away from normality • fewer assumptions • extended phenotypes • check robustness • multiple crosses Goals how many QTL? • inferring the number • sampling all QT loci • estimating heritability October 2001 Jackson Labs Workshop © Brian S. Yandell 3 ..."
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away from normality • fewer assumptions • extended phenotypes • check robustness • multiple crosses Goals how many QTL? • inferring the number • sampling all QT loci • estimating heritability October 2001 Jackson Labs Workshop © Brian S. Yandell 3
Size distribution of geological faults: Model choice and parameter estimation
"... Abstract: Geological faults are important in reservoir characterization, since they in�uence �uid �ow in the reservoir. Both the number of faults, or the fault intensity, and the fault sizes are of importance. Fault sizes are often represented by maximum displacements, which can be interpreted from ..."
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Abstract: Geological faults are important in reservoir characterization, since they in�uence �uid �ow in the reservoir. Both the number of faults, or the fault intensity, and the fault sizes are of importance. Fault sizes are often represented by maximum displacements, which can be interpreted from seismic data. Owing to limitations in seismic resolution only faults of relativelylarge size can be observed, and the observations are biased. In order to make inference about the overall fault population, a proper model must be chosen for the fault size distribution. A fractal (Pareto) distribution is commonly used in geophysics literature, but the exponential distribution has also been suggested. In this work we compare the two models statistically. A Bayesian model is de�ned for the fault size distributions under the two competing models, where the prior distributions are given as the Pareto and the exponential pdfs, respectively, and the likelihood function describes the sampling errors associated with seismic fault observations. The Bayes factor is used
Network analysis of measles data 1 A Networkbased Analysis of the 1861 Hagelloch Measles Data
"... In this article, we demonstrate a statistical method for fitting the parameters of a sophisticated network and epidemic model to disease data. The pattern of contacts between hosts is described by a class of Exponentialfamily Random Graph Models (ERGMs) while the transmission process that runs over ..."
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In this article, we demonstrate a statistical method for fitting the parameters of a sophisticated network and epidemic model to disease data. The pattern of contacts between hosts is described by a class of Exponentialfamily Random Graph Models (ERGMs) while the transmission process that runs over the network is modeled as a stochastic SusceptibleExposedInfectiousRemoved (SEIR) epidemic. We fit these models to very detailed data from a 1861 measles outbreak in Hagelloch, Germany. The network models include parameters for all recorded host covariates including age, sex, household and classroom membership and household location while the SEIR epidemic model has exponentially distributed transmission times with gamma distributed latent and infective periods. This approach allows us to make meaningful statements about the structure of the population—separate from the transmission process— as well as to provide estimates of various biological quantities of interest, such as the basic reproductive number, R0. Using reversible jump Markov chain Monte Carlo, we produce samples from the joint posterior distribution of all the parameters of this model—the network, transmission tree, ERGM parameters and SEIR parameters—and perform Bayesian model selection to find the bestfitting network model. We
Original Article
, 2003
"... Evolutionary morphology of the coelurosaurian arctometatarsus: descriptive, morphometric and phylogenetic approaches ..."
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Evolutionary morphology of the coelurosaurian arctometatarsus: descriptive, morphometric and phylogenetic approaches
Supplemental Material for A Bayesian Partition Method for Detecting Pleiotropic and Epistatic eQTL Modules
"... Suppose we have a sample of N individuals. Each individual i is measured with G gene expression values denoted as { y: g � 1,..., G} and M marker genotypes denoted as ig { xim: m � 1,..., M}. We partition the data into D modules { d: d � 1,..., D} plus a null component { d: d � 0} where the number o ..."
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Suppose we have a sample of N individuals. Each individual i is measured with G gene expression values denoted as { y: g � 1,..., G} and M marker genotypes denoted as ig { xim: m � 1,..., M}. We partition the data into D modules { d: d � 1,..., D} plus a null component { d: d � 0} where the number of modules, D, is prespecified. Each module d consists of G d n genes and M n associated markers. Genes and markers that have no d associations are partitioned into the null component. We further partition the N individuals into T d n types { : 1,...,} T t t � n with respect to each module d. Different d modules may have a different number of individual types as well as different individual partitions. The overall partition of genes and markers into modules is determined by the gene indicators { I: g �1,... N, I � {0,1,.., D}} and the marker indicators g g
Retracing MicroEpidemics of Chagas Disease Using Epicenter Regression
"... Vectorborne transmission of Chagas disease has become an urban problem in the city of Arequipa, Peru, yet the debilitating symptoms that can occur in the chronic stage of the disease are rarely seen in hospitals in the city. The lack of obvious clinical disease in Arequipa has led to speculation th ..."
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Vectorborne transmission of Chagas disease has become an urban problem in the city of Arequipa, Peru, yet the debilitating symptoms that can occur in the chronic stage of the disease are rarely seen in hospitals in the city. The lack of obvious clinical disease in Arequipa has led to speculation that the local strain of the etiologic agent, Trypanosoma cruzi, has low chronic pathogenicity. The long asymptomatic period of Chagas disease leads us to an alternative hypothesis for the absence of clinical cases in Arequipa: transmission in the city may be so recent that most infected individuals have yet to progress to late stage disease. Here we describe a new method, epicenter regression, that allows us to infer the spatial and temporal history of disease transmission from a snapshot of a population’s infection status. We show that in a community of Arequipa, transmission of T. cruzi by the insect vector Triatoma infestans occurred as a series of focal microepidemics, the oldest of which began only around 20 years ago. These microepidemics infected nearly 5 % of the community before
Bayesian Random Segmentation Models to Identify Shared Copy Number Aberrations for Array CGH Data
, 2010
"... Arraybased comparative genomic hybridization (aCGH) is a highresolution highthroughput technique for studying the genetic basis of cancer. The resulting data consists of log fluorescence ratios as a function of the genomic DNA location and provides a cytogenetic representation of the relative DNA ..."
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Arraybased comparative genomic hybridization (aCGH) is a highresolution highthroughput technique for studying the genetic basis of cancer. The resulting data consists of log fluorescence ratios as a function of the genomic DNA location and provides a cytogenetic representation of the relative DNA copy number variation. Analysis of such data typically involves estimation of the underlying copy number state at each location and segmenting regions of DNA with similar copy number states. Most current methods proceed by modeling a single sample/array at a time, and thus fail to borrow strength across multiple samples to infer shared regions of copy number aberrations. We propose a hierarchical Bayesian random segmentation approach for modeling aCGH data that utilizes information across arrays from a common population to yield segments of shared copy number changes. These changes characterize the underlying population and allow us to compare different population aCGH profiles to assess which regions of the genome have differential alterations. Our method, referred to as BDSAcgh (Bayesian Detection of Shared Aberrations in aCGH), is based on a unified Bayesian hierarchical model that allows us to obtain probabilities of alteration states as well as probabilities of differential alteration that correspond to