Spatial clustering of average risks and risk trends in Bayesian disease mapping

Spatiotemporal disease mapping focuses on estimating the spatial pattern in disease risk across a set of nonoverlapping areal units over a fixed period of time. The key aim of such research is to identify areas that have a high average level of disease risk or where disease risk is increasing over time, thus allowing public health interventions to be focused on these areas. Such aims are well suited to the statistical approach of clustering, and while much research has been done in this area in a purely spatial setting, only a handful of approaches have focused on spatiotemporal clustering of disease risk. Therefore, this paper outlines a new modeling approach for clustering spatiotemporal disease risk data, by clustering areas based on both their mean risk levels and the behavior of their temporal trends. The efficacy of the methodology is established by a simulation study, and is illustrated by a study of respiratory disease risk in Glasgow, Scotland.     

Localization of a quantitative trait locus via a Bayesian approach

A Bayesian approach to the direct mapping of a quantitative trait locus (QTL), fully utilizing information from multiple linked gene markers, is presented in this paper. The joint posterior distribution (a mixture distribution modeling the linkage between a biallelic QTL and N gene markers) is computationally challenging and invites exploration via Markov chain Monte Carlo methods. The parameter's complete marginal posterior densities are obtained, allowing a diverse range of inferences. Parameters estimated include the QTL genotype probabilities for the sires and the offspring, the allele frequencies for the QTL, and the position and additive and dominance effects of the QTL. The methodology is applied through simulation to a half-sib design to form an outbred pedigree structure where there is an entire class of missing information. The capacity of the technique to accurately estimate parameters is examined for a range of scenarios.     

Modeling a Poisson forest in variable elevations: a nonparametric Bayesian approach

A nonparametric Bayesian formulation is given to the problem of modeling nonhomogeneous spatial point patterns influenced by concomitant variables. Only incomplete information on the concomitant variables is assumed, consisting of a relatively small number of point measurements. Residual variation, caused by other unmeasured influential factors, is modeled in terms of a spatially varying baseline intensity function. A Markov chain Monte Carlo scheme is proposed for the simultaneous nonparametric estimation of each unknown function in the model. The suggested method is illustrated by reanalysing a data set in Rathbun (1996, Biometrics 52, 226-242), and the estimated models are compared with those obtained by Rathbun.     

Bayesian nonparametric estimation of continuous monotone functions with applications to dose-response analysis

Summary .  In this article, we consider monotone nonparametric regression in a Bayesian framework. The monotone function is modeled as a mixture of shifted and scaled parametric probability distribution functions, and a general random probability measure is assumed as the prior for the mixing distribution. We investigate the choice of the underlying parametric distribution function and find that the two-sided power distribution function is well suited both from a computational and mathematical point of view. The model is motivated by traditional nonlinear models for dose–response analysis, and provides possibilities to elicitate informative prior distributions on different aspects of the curve. The method is compared with other recent approaches to monotone nonparametric regression in a simulation study and is illustrated on a data set from dose–response analysis.     

Bayesian Partitioning for Modeling and Mapping Spatial Case–Control Data

Summary Methods for modeling and mapping spatial variation in disease risk continue to motivate much research. In particular, spatial analyses provide a useful tool for exploring geographical heterogeneity in health outcomes, and consequently can yield clues as to disease etiology, direct public health management, and generate research hypotheses. This article presents a Bayesian partitioning approach for the analysis of individual level geo‐referenced health data. The model makes few assumptions about the underlying form of the risk surface, is data adaptive, and allows for the inclusion of known determinants of disease. The methodology is used to model spatial variation in neonatal mortality in Porto Alegre, Brazil.     

Monitoring changes in spatio-temporal maps of disease

The object of statistical surveillance is to detect a change in a process accurately and quickly as new observations keep adding to the observed part of the process. In this paper we discuss methodological issues in developing a rapid response in a spatial surveillance system. Simple exploratory statistical methods together with more sophisticated methods, based on hierarchical space-time models defined at small area level, are considered.     

Bayesian detection and modeling of spatial disease clustering

Many current statistical methods for disease clustering studies are based on a hypothesis testing paradigm. These methods typically do not produce useful estimates of disease rates or cluster risks. In this paper, we develop a Bayesian procedure for drawing inferences about specific models for spatial clustering. The proposed methodology incorporates ideas from image analysis, from Bayesian model averaging, and from model selection. With our approach, we obtain estimates for disease rates and allow for greater flexibility in both the type of clusters and the number of clusters that may be considered. We illustrate the proposed procedure through simulation studies and an analysis of the well-known New York leukemia data.     

Comparison of Bayesian methods for flexible modeling of spatial risk surfaces in disease mapping

Bayesian hierarchical models usually model the risk surface on the same arbitrary geographical units for all data sources. Poisson/gamma random field models overcome this restriction as the underlying risk surface can be specified independently to the resolution of the data. Moreover, covariates may be considered as either excess or relative risk factors. We compare the performance of the Poisson/gamma random field model to the Markov random field (MRF)‐based ecologic regression model and the Bayesian Detection of Clusters and Discontinuities (BDCD) model, in both a simulation study and a real data example. We find the BDCD model to have advantages in situations dominated by abruptly changing risk while the Poisson/gamma random field model convinces by its flexibility in the estimation of random field structures and by its flexibility incorporating covariates. The MRF‐based ecologic regression model is inferior. WinBUGS code for Poisson/gamma random field models is provided.     

Modeling Inter‐Subject Variability in fMRI Activation Location: A Bayesian Hierarchical Spatial Model

Summary The aim of this article is to develop a spatial model for multi‐subject fMRI data. There has been extensive work on univariate modeling of each voxel for single and multi‐subject data, some work on spatial modeling of single‐subject data, and some recent work on spatial modeling of multi‐subject data. However, there has been no work on spatial models that explicitly account for inter‐subject variability in activation locations. In this article, we use the idea of activation centers and model the inter‐subject variability in activation locations directly. Our model is specified in a Bayesian hierarchical framework which allows us to draw inferences at all levels: the population level, the individual level, and the voxel level. We use Gaussian mixtures for the probability that an individual has a particular activation. This helps answer an important question that is not addressed by any of the previous methods: What proportion of subjects had a significant activity in a given region. Our approach incorporates the unknown number of mixture components into the model as a parameter whose posterior distribution is estimated by reversible jump Markov chain Monte Carlo. We demonstrate our method with a fMRI study of resolving proactive interference and show dramatically better precision of localization with our method relative to the standard mass‐univariate method. Although we are motivated by fMRI data, this model could easily be modified to handle other types of imaging data.     

On the Bayesian estimation of a closed population size in the presence of heterogeneity and model uncertainty

Summary .   We consider the estimation of the size of a closed population, often of interest for wild animal populations, using a capture–recapture study. The estimate of the total population size can be very sensitive to the choice of model used to fit to the data. We consider a Bayesian approach, in which we consider all eight plausible models initially described by Otis et al. (1978, Wildlife Monographs 62, 1–135) within a single framework, including models containing an individual heterogeneity component. We show how we are able to obtain a model-averaged estimate of the total population, incorporating both parameter and model uncertainty. To illustrate the methodology we initially perform a simulation study and analyze two datasets where the population size is known, before considering a real example relating to a population of dolphins off northeast Scotland.     

Polytomous disease mapping to detect uncommon risk factors for related diseases

A statistical model for jointly analysing the spatial variation of incidences of three (or more) diseases, with common and uncommon risk factors, is introduced. Deaths for different diseases are described by a logit model for multinomial responses (multinomial logit or polytomous logit model). For each area and confounding strata population (i.e. age-class, sex, race) the probabilities of death for each cause (the response probabilities) are estimated. A specic disease, the one having a common risk factor only, acts as the baseline category. The log odds are decomposed additively into shared (common to diseases different by the reference disease) and specic structured spatial variability terms, unstructured unshared spatial terms and confounders terms (such as age, race and sex) to adjust the crude observed data for their effects. Disease specic spatially structured effects are estimated; these are considered as latent variables denoting disease-specic risk factors. The model is presented with reference to a specic application. We considered the mortality data (from 1990 to 1994) relative to oral cavity, larynx and lung cancers in 13 age groups of males, in the 287 municipalities of Region of Tuscany (Italy). All these pathologies share smoking as a common risk factor; furthermore, two of them (oral cavity and larynx cancer) share alcohol consumption as a risk factor. All studies suggest that smoking and alcohol consumption are the major known risk factors for oral cavity and larynx cancers; nevertheless, in this paper, we investigate the possibility of other different risk factors for these diseases, or even the presence of an interaction effect (between smoking and alcohol risk factors) but with different spatial patterns for oral and larynx cancer. For each municipality and age-class the probabilities of death for each cause (the response probabilities) are estimated. Lung cancer acts as the baseline category. The log odds are decomposed additively into shared (common to oral cavity and larynx diseases) and specic structured spatial variability terms, unstructured unshared spatial terms and an age-group term. It turns out that oral cavity and larynx cancer have different spatial patterns for residual risk factors which are not the typical ones such as smoking habits and alcohol consumption. But, possibly, these patterns are due to different spatial interactions between smoking habits and alcohol consumption for the first and the second disease.     

Simultaneous estimation of multiple quantitative trait loci and growth curve parameters through hierarchical Bayesian modeling

A novel hierarchical quantitative trait locus (QTL) mapping method using a polynomial growth function and a multiple-QTL model (with no dependence in time) in a multitrait framework is presented. The method considers a population-based sample where individuals have been phenotyped (over time) with respect to some dynamic trait and genotyped at a given set of loci. A specific feature of the proposed approach is that, instead of an average functional curve, each individual has its own functional curve. Moreover, each QTL can modify the dynamic characteristics of the trait value of an individual through its influence on one or more growth curve parameters. Apparent advantages of the approach include: (1) assumption of time-independent QTL and environmental effects, (2) alleviating the necessity for an autoregressive covariance structure for residuals and (3) the flexibility to use variable selection methods. As a by-product of the method, heritabilities and genetic correlations can also be estimated for individual growth curve parameters, which are considered as latent traits. For selecting trait-associated loci in the model, we use a modified version of the well-known Bayesian adaptive shrinkage technique. We illustrate our approach by analysing a sub sample of 500 individuals from the simulated QTLMAS 2009 data set, as well as simulation replicates and a real Scots pine (Pinus sylvestris) data set, using temporal measurements of height as dynamic trait of interest.     

A Bayesian change-point analysis of electromyographic data: detecting muscle activation patterns and associated applications

Many facets of neuromuscular activation patterns and control can be assessed via electromyography and are important for understanding the control of locomotion. After spinal cord injury, muscle activation patterns can affect locomotor recovery. We present a novel application of reversible jump Markov chain Monte Carlo simulation to estimate activation patterns from electromyographic data. We assume the data to be a zero-mean, heteroscedastic process. The variance is explicitly modeled using a step function. The number and location of points of discontinuity, or change-points, in the step function, the inter-change-point variances, and the overall mean are jointly modeled along with the mean and variance from baseline data. The number of change-points is considered a nuisance parameter and is integrated out of the posterior distribution. Whereas current methods of detecting activation patterns are deterministic or provide only point estimates, ours provides distributional estimates of muscle activation. These estimates, in turn, are used to estimate physiologically relevant quantities such as muscle coactivity, total integrated energy, and average burst duration and to draw valid statistical inferences about these quantities.     

Induction of PrPSc-specific systemic and mucosal immune responses in white-tailed deer with an oral vaccine for chronic wasting disease

The ongoing epidemic of chronic wasting disease (CWD) within cervid populations indicates the need for novel approaches for disease management. A vaccine that either reduces susceptibility to infection or reduces shedding of prions by infected animals, or a combination of both, could be of benefit for disease control. The development of such a vaccine is challenged by the unique nature of prion diseases and the requirement for formulation and delivery in an oral format for application in wildlife settings. To address the unique nature of prions, our group targets epitopes, termed disease specific epitopes (DSEs), whose exposure for antibody binding depends on disease-associated misfolding of PrP^C into PrP^Sc. Here, a DSE corresponding to the rigid loop (RL) region, which was immunogenic following parenteral vaccination, was translated into an oral vaccine. This vaccine consists of a replication-incompetent human adenovirus expressing a truncated rabies glycoprotein G recombinant fusion with the RL epitope (hAd5:tgG-RL). Oral immunization of white-tailed deer with hAd5:tgG-RL induced PrP^Sc-specific systemic and mucosal antibody responses with an encouraging safety profile in terms of no adverse health effects nor prolonged vector shedding. By building upon proven strategies of formulation for wildlife vaccines, these efforts generate a particular PrP^Sc-specific oral vaccine for CWD as well as providing a versatile platform, in terms of carrier protein and biological vector, for generation of other oral, peptide-based CWD vaccines.     

Synthesis and cytotoxicity against KB and NCI-H187 cell lines of sporogen AO-1 analogues

20 Analogues of sporogen AO-1 were synthesized by chemical modification at α,β-unsaturated carbonyl, 3-hydroxyl and vinylic methyl groups of sporogen AO-1 precursor, and were evaluated for their cytotoxic activities against human oral epidermoid carcinoma (KB) and human small cell lung (NCI-H187) cancer cell lines. Structure-activity relationship study indicated the importance of α,β-unsaturated carbonyl moiety for both cancer cell lines. Vinylic methyl and R-configuration of 3-hydroxyl group were crucial for cytotoxicity toward KB cells. In contrast, conversion of vinylic methyl and 3-hydroxyl groups to ketone moieties afforded triketone 19 which displayed comparable cytotoxicity against NCI-H187 cells lines to sporogen AO-1, and was more potent than ellipticine, a standard drug. Interestingly, compound 19 was weakly cytotoxic toward Vero cells, whereas sporogen AO-1 showed strong cytotoxicity.     

Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage

Here we focus on discrimination problems where the number of predictors substantially exceeds the sample size and we propose a Bayesian variable selection approach to multinomial probit models. Our method makes use of mixture priors and Markov chain Monte Carlo techniques to select sets of variables that differ among the classes. We apply our methodology to a problem in functional genomics using gene expression profiling data. The aim of the analysis is to identify molecular signatures that characterize two different stages of rheumatoid arthritis.     

Bayesian population dynamics of interacting species: great gerbils and fleas in Kazakhstan

We propose a discrete-time Bayesian hierarchical model for the population dynamics of the great gerbil-flea ecological system. The model accounts for the sampling variability arising from data originally collected for other purposes. The prior for the unknown population densities incorporates specific biological hypotheses regarding the interacting dynamics of the two species, as well as their life cycles, where density-dependent effects are included. Posterior estimates are obtained via Markov chain Monte Carlo. The variance of the observed density estimates is a quadratic function of the unknown density. Our study indicates the presence of a density-dependent growth rate for the gerbil population. For the flea population there is clear evidence of density-dependent over-summer net growth, which is dependent on the flea-to-gerbil ratio at the beginning of the reproductive summer. Over-winter net growth is favored by high density. We estimate that on average 35% of the gerbil population survives the winter. Our study shows that hierarchical Bayesian models can be useful in extracting ecobiological information from observational data.     

健康人群口腔酵母菌的分离及菌种分布

目的调查健康人群口腔酵母菌的分布。方法将来源于健康人的口腔拭子接种于改良SDA培养基,37℃培养14d,分到的酵母菌通过菌落形态、革兰染色作初步鉴定．芽管形成试验、厚膜孢子生成及假菌丝产生试验、YBC酵母鉴定卡作菌种鉴定。结果酵母菌在健康人口腔中总分离率为8．78％(86／979),其中较多的为白色假丝酵母菌37株,占43．02％,近平滑假丝酵母菌和季也蒙假丝酵母菌各9株,各占10．47％,葡萄牙假丝酵母菌6株,占6．98％,其他25株。结论健康人口腔中有多种条件致病酵母菌的寄生。     

A Bayesian multi-risks survival (MRS) model in the presence of double censorings

Semi-competing risks data include the time to a nonterminating event and the time to a terminating event, while competing risks data include the time to more than one terminating event. Our work is motivated by a prostate cancer study, which has one nonterminating event and two terminating events with both semi-competing risks and competing risks present as well as two censoring times. In this paper, we propose a new multi-risks survival (MRS) model for this type of data. In addition, the proposed MRS model can accommodate noninformative right-censoring times for nonterminating and terminating events. Properties of the proposed MRS model are examined in detail. Theoretical and empirical results show that the estimates of the cumulative incidence function for a nonterminating event may be biased if the information on a terminating event is ignored. A Markov chain Monte Carlo sampling algorithm is also developed. Our methodology is further assessed using simulations and also an analysis of the real data from a prostate cancer study. As a result, a prostate-specific antigen velocity greater than 2.0 ng/mL per year and higher biopsy Gleason scores are positively associated with a shorter time to death due to prostate cancer.