Modelling multivariate disease rates with a latent structure mixture model

Copyright © 2013 SAGE / Statistical Modeling SocietyThere has been considerable recent interest in multivariate modelling of the geographical distribution of morbidity or mortality rates for potentially related diseases. The motivations for this include investigation of similarities or dissimilarities in the risk distribution for the different diseases, as well as ‘borrowing strength’ across disease rates to shrink the uncertainty in geographical risk assessment for any particular disease. A number of approaches to such multivariate modelling have been suggested and this paper proposes an extension to these which may provide a richer range of dependency structures than those encompassed so far. We develop a model which incorporates a discrete mixture of latent structures and argue that this provides potential to represent an enhanced range of correlation structures between diseases at the same time as implicitly allowing for less restrictive spatial correlation structures between geographical units. We compare and contrast our approach to other commonly used multivariate disease models and demonstrate comparative results using data taken from cancer registries on four carcinomas in some 300 geographical units in England, Scotland and Wales

Grammar for Writing? An investigation into the effect of Contextualised Grammar Teaching on Student Writing

publication-status: Publishedtypes: ArticleThe role of grammar instruction in the teaching of writing is contested in most Anglophone countries, with several robust meta-analyses finding no evidence of any beneficial effect. However, existing research is limited in that it only considers isolated grammar instruction and offers no theorisation of an instructional relationship between grammar and writing. This study, drawing on a theorised understanding of grammar as a meaning-making resource for writing development, set out to investigate the impact of contextualised grammar instruction on students’ writing performance. The study adopted a mixed-methods approach, with a randomised controlled trial and a complementary qualitative study. The statistical analyses indicate a positive effect on writing performance for the intervention group (e = 0.21; p<0.001); but the study also indicates that the intervention impact differentially on different sub-groups, benefiting able writers more than weaker writers. The study is significant in being the first to supply rigorous, theorised evidence for the potential benefits of teaching grammar to support development in writing

The interactions between municipal socioeconomic status and age on hip fracture risk

The final publication is available at Springer via http://dx.doi.org/10.1007/s00198-014-2869-0SUMMARY: Age modifies the effect of area-level socioeconomic status (SES) in the risk of fragility hip fractures (HF). For older individuals, the risk of HF increases as SES increases. For younger individuals, risk of HF increases as SES decreases. Our study may help decision-makers to better direct the implementation of political decisions. INTRODUCTION: The effect of socioeconomic status (SES) on hip fracture (HF) incidence remains unclear. The objective of this study is to evaluate the association between HF incidence and municipality-level SES as well as interactions between age and SES. METHODS: From the Portuguese Hospital Discharge Database, we selected hospitalizations (2000-2010) of patients aged 50+, with HF diagnosis (codes 820.x, ICD9-CM), caused by traumas of low/moderate energy, excluding bone cancer cases and readmissions for aftercare. Municipalities were classified according to SES (deprived to affluent) using 2001 Census data. A spatial Bayesian hierarchical regression model (controlling for data heterogeneity and spatial autocorrelation), using the Poisson distribution, was used to quantify the relative risk (RR) of HF, 95% credible interval (95%CrI), and analyze the interaction between age and SES after adjusting for rural conditions. RESULTS: There were 96,905 HF, 77.3% of which were on women who, on average, were older than men (mean age 81.2±8.5 vs 78.2±10.1 years) at admission (p<0.001). In women, there was a lower risk associated with better SES: RR=0.83 (95%CrI 0.65-1.00) for affluent versus deprived. There was an inverse association between SES and HF incidence rate in the youngest and a direct association in the oldest, for both sexes, but significant only between deprived and affluent in older ages (≥75 years). CONCLUSIONS: Interaction between SES and age may be due to inequalities in lifestyles, access to health systems, and preventive actions. These results may help decision-makers to better understand the epidemiology of hip fractures and to better direct the available funding.Programa Operacional Factores de Competitividade (COMPETE)Fundação para a Ciência e a Tecnologia (FCT

Biclustering models for structured microarray data

©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Microarrays have become a standard tool for investigating gene function and more complex microarray experiments are increasingly being conducted. For example, an experiment may involve samples from several groups or may investigate changes in gene expression over time for several subjects, leading to large three-way data sets. In response to this increase in data complexity, we propose some extensions to the plaid model, a biclustering method developed for the analysis of gene expression data. This model-based method lends itself to the incorporation of any additional structure such as external grouping or repeated measures. We describe how the extended models may be fitted and illustrate their use on real data

On Evidence Weighted Mixture Classification

2005 Joint Annual Meeting of the Interface and the Classification Society of North America, St. Louis, Missouri, 8-12 June 2005Calculation of the marginal likelihood or evidence is a problem central to model selection and model averaging in a Bayesian framework. Many sampling methods, especially (Reversible Jump) Markov chain Monte Carlo techniques, have been devised to avoid explicit calculation of the evidence, but they are limited to models with a common parameterisation. It is desirable to extend model averaging to models with disparate architectures and parameterisations. In this paper we present a straightforward general computational scheme for calculating the evidence, applicable to any model for which samples can be drawn from the posterior distribution of parameters conditioned on the data. The scheme is demonstrated on a simple feature subset selection example

Transmission parameters estimated for Salmonella typhimurium in swine using susceptible-infectious-resistant models and a Bayesian approach.

This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.BACKGROUND: Transmission models can aid understanding of disease dynamics and are useful in testing the efficiency of control measures. The aim of this study was to formulate an appropriate stochastic Susceptible-Infectious-Resistant/Carrier (SIR) model for Salmonella Typhimurium in pigs and thus estimate the transmission parameters between states. RESULTS: The transmission parameters were estimated using data from a longitudinal study of three Danish farrow-to-finish pig herds known to be infected. A Bayesian model framework was proposed, which comprised Binomial components for the transition from susceptible to infectious and from infectious to carrier; and a Poisson component for carrier to infectious. Cohort random effects were incorporated into these models to allow for unobserved cohort-specific variables as well as unobserved sources of transmission, thus enabling a more realistic estimation of the transmission parameters. In the case of the transition from susceptible to infectious, the cohort random effects were also time varying. The number of infectious pigs not detected by the parallel testing was treated as unknown, and the probability of non-detection was estimated using information about the sensitivity and specificity of the bacteriological and serological tests. The estimate of the transmission rate from susceptible to infectious was 0.33 [0.06, 1.52], from infectious to carrier was 0.18 [0.14, 0.23] and from carrier to infectious was 0.01 [0.0001, 0.04]. The estimate for the basic reproduction ration (R0) was 1.91 [0.78, 5.24]. The probability of non-detection was estimated to be 0.18 [0.12, 0.25]. CONCLUSIONS: The proposed framework for stochastic SIR models was successfully implemented to estimate transmission rate parameters for Salmonella Typhimurium in swine field data. R0 was 1.91, implying that there was dissemination of the infection within pigs of the same cohort. There was significant temporal-cohort variability, especially at the susceptible to infectious stage. The model adequately fitted the data, allowing for both observed and unobserved sources of uncertainty (cohort effects, diagnostic test sensitivity), so leading to more reliable estimates of transmission parameters.FC

Demographic buffering and compensatory recruitment promotes the persistence of disease in a wildlife population.

Published onlineLETTERDemographic buffering allows populations to persist by compensating for fluctuations in vital rates, including disease-induced mortality. Using long-term data on a badger (Meles meles Linnaeus, 1758) population naturally infected with Mycobacterium bovis, we built an integrated population model to quantify impacts of disease, density and environmental drivers on survival and recruitment. Badgers exhibit a slow life-history strategy, having high rates of adult survival with low variance, and low but variable rates of recruitment. Recruitment exhibited strong negative density-dependence, but was not influenced by disease, while adult survival was density independent but declined with increasing prevalence of diseased individuals. Given that reproductive success is not depressed by disease prevalence, density-dependent recruitment of cubs is likely to compensate for disease-induced mortality. This combination of slow life history and compensatory recruitment promotes the persistence of a naturally infected badger population and helps to explain the badger's role as a persistent reservoir of M. bovis.NERCUK Department of Environment, Food and Rural Affair

Computing with confidence: a Bayesian approach

Bayes’ rule is introduced as a coherent strategy for multiple recomputations of classifier system output, and thus as a basis for assessing the uncertainty associated with a particular system results --- i.e. a basis for confidence in the accuracy of each computed result. We use a Markov-Chain Monte Carlo method for efficient selection of recomputations to approximate the computationally intractable elements of the Bayesian approach. The estimate of the confidence to be placed in any classification result provides a sound basis for rejection of some classification results. We present uncertainty envelopes as one way to derive these confidence estimates from the population of recomputed results. We show that a coarse SURE or UNSURE confidence rating based on a threshold of agreed classifications works well, not only pinpointing those results that are reliable but also in indicating input data problems, such as corrupted or incomplete data, or application of an inadequate classifier model

Representing classifier confidence in the safety critical domain: an illustration from mortality prediction in trauma cases

Copyright © 2007 Springer Verlag. The final publication is available at link.springer.comThis work proposes a novel approach to assessing confidence measures for software classification systems in demanding applications such as those in the safety critical domain. Our focus is the Bayesian framework for developing a model-averaged probabilistic classifier implemented using Markov chain Monte Carlo (MCMC) and where appropriate its reversible jump variant (RJ-MCMC). Within this context we suggest a new technique, building on the reject region idea, to identify areas in feature space that are associated with "unsure" classification predictions. We term such areas "uncertainty envelopes" and they are defined in terms of the full characteristics of the posterior predictive density in different regions of the feature space. We argue this is more informative than use of a traditional reject region which considers only point estimates of predictive probabilities. Results from the method we propose are illustrated on synthetic data and also usefully applied to real life safety critical systems involving medical trauma data

A Bayesian methodology for estimating uncertainty of decisions in safety-critical systems

Published as chapter in Frontiers in Artificial Intelligence and Applications. Volume 149, IOS Press Book, 2006. Integrated Intelligent Systems for Engineering Design. Edited by Xuan F. Zha, R.J. Howlett. ISBN 978-1-58603-675-1, pp. 82-96. This version deposited in arxiv.orghttp://arxiv.org/abs/1012.0322Uncertainty of decisions in safety-critical engineering applications can be estimated on the basis of the Bayesian Markov Chain Monte Carlo (MCMC) technique of averaging over decision models. The use of decision tree (DT) models assists experts to interpret causal relations and find factors of the uncertainty. Bayesian averaging also allows experts to estimate the uncertainty accurately when a priori information on the favored structure of DTs is available. Then an expert can select a single DT model, typically the Maximum a Posteriori model, for interpretation purposes. Unfortunately, a priori information on favored structure of DTs is not always available. For this reason, we suggest a new prior on DTs for the Bayesian MCMC technique. We also suggest a new procedure of selecting a single DT and describe an application scenario. In our experiments on the Short-Term Conflict Alert data our technique outperforms the existing Bayesian techniques in predictive accuracy of the selected single DTs.Supported by a grant from the EPSRC under the Critical Systems Program, grant GR/R24357/0