A parametric model for estimating prevalence,incidence, and mean bout duration from point sampling

Point or instantaneous sampling refers to the scoring of presence or absence of behavior at the end of equally spaced intervals of time and is used to estimate prevalence. The literature cited demonstrates that point sampling does not adequately estimate frequency or mean bout duration. A parametric model is developed based on exponentially distributed times of behavior and intervening nonbehavior, thus enabling estimators of mean bout length and incidence. Variance estimators are provided and a method is suggested for designing sample situations which control the variance of the prevalence estimator. The paper concludes the theoretical investigation with a thorough Monte Carlo investigation and application to a “real-life” problem. The point sample estimators compare favorably with continuous observation under appropriate choice of sampling interval and under approximately exponential assumptions.     

Estimating the distribution of a renewal process from times at which events from an independent process are detected

The analysis of length-biased data has been mostly limited to the interarrival interval of a renewal process covering a specific time point. Motivated by a surveillance problem, we consider a more general situation where this time point is random and related to a specific event, for example, status change or onset of a disease. We also consider the problem when additional information is available on whether the event intervals (interarrival intervals covering the random event) end within or after a random time period (which we call a window period) following the random event. Under the assumptions that the occurrence rate of the random event is low and the renewal process is independent of the random event, we provide formulae for the estimation of the distribution of interarrival times based on the observed event intervals. Procedures for testing the required assumptions are also furnished. We apply our results to human immunodeficiency virus (HIV) test data from public test sites in Seattle, Washington, where the random event is HIV infection and the window period is from the onset of HIV infection to the time at which a less sensitive HIV test becomes positive. Results show that the estimator of the intertest interval length distribution from event intervals ending within the window period is less biased than the estimator from all event intervals; the latter estimator is affected by right truncation. Finally, we discuss possible applications to estimating HIV incidence and analyzing length-biased samples with right or left truncated data.     

On a Martingale Approach for the Estimation of the Parameters of a Bivariate Carrier Borne Epidemic Model in Presence of a Linear Relationship Between Carriers and Suspectibles

The present investigation is to waive the restriction of PICARD (1980) viz. x₀ + y₀ = n (the size of the population) for the obvious reason that the size of the susceptible population exposed to risk will depend on the growth rate of carriers and to evolve a simple methodology for the estimation of parameters of Downton's bivariate model. A lienar relationship of X_t on Y_t enabled to predict the size of the susceptible population exposed to the risk by a given number of carriers at any time. The same linear equation, when incorporated in Downton's bivariate model has generated the marginal process of Y_t. Using several Martingales constructed on the basis of the above marginal process, the minimum number of carriers that can bring out an epidemic and various parameters of Downton's model have been estimated.     

Bayesian monitoring of clinical trials with failure-time endpoints

This article presents an aid for monitoring clinical trials with failure-time endpoints based on the Bayesian nonparametric analyses of the data. The posterior distribution is a mixture of Dirichlet processes in the presence of censoring if one assumes a Dirichlet process prior for the survival distribution. Using Gibbs sampling, one can generate random samples from the posterior distribution. With samples from the posterior distributions of treatment-specific survival curves, one can evaluate the current evidence in favor of stopping or continuing the trial based on summary statistics of these survival curves. Because the method is nonparametric, it can easily be used, for example, in situations where hazards cross or are suspected to cross and where relevant clinical decisions might be based on estimating when the integral between the curves might be expected to become positive and in favor of the new but toxic therapy. An example based on an actual trial illustrates the method.     

A Martingale Approach to the Investigation of the Duration of a Mild Epidemic

The present paper is an attempt to evolve a simple methodology to obtain the steady state solution of the duration of a mild epidemic (like conjunctivitis, for which the period of sickness is about a week and the period of immunity following recovery is comparatively large) and also the intensity of the epidemic measured by the number of infections during the complete spell of the epidemic. The methodology consists of constructing Martingales based on subcritical and supercritical forms of a Simple Branching Process; and obtaining the stopping time enabling to evaluate the expected duration of the epidemic and the sampling variance together with the intensity of the epidemic.     

A note on semiparametric efficient inference for two-stage outcome-dependent sampling with a continuous outcome

Outcome-dependent sampling designs have been shown to be a cost-effectiveway to enhance study efficiency. We show that the outcome-dependentsampling design with a continuous outcome can be viewed as anextension of the two-stage case-control designs to the continuous-outcomecase. We further show that the two-stage outcome-dependent samplinghas a natural link with the missing-data and biased-samplingframeworks. Through the use of semiparametric inference andmissing-data techniques, we show that a certain semiparametricmaximum-likelihood estimator is computationally convenient andachieves the semiparametric efficient information bound. Wedemonstrate this both theoretically and through simulation.     

不同降压过程对深海海水中可培养细菌群落组成的影响

【目的】控制不同的压力变化过程,比较对深海水样中可培养细菌组成的影响,探讨马里亚纳海沟深海水样中可培养细菌在不同降压处理过程下的丰度变化和群落组成。【方法】利用保压技术采集无污染、深度6001 m的深海水样后,模拟缓慢降压和快速降压过程,通过2216E培养基及2216E加氧化三甲胺(TMAO)富集培养基,对分离得到的可培养菌株进行16S rRNA基因测序分析和丰度检测。【结果】通过缓慢降压和快速降压处理后,深海海水样品中可培养细菌的丰度和群落组成差异较大。其中,在缓慢降压处理的样品中,平均丰度约为190 CFU/mL,且种群组成单一,以Bacillus属为主(占总菌落数的96%);而快速降压处理的样品中,平均丰度约为437 CFU/mL,主要分布在4个属中:Bacillus (占总菌落数的27.8%)、Achromobacter (24.4%)、Microbacterium (34.4%)和Pseudomonas (13.7%)。值得一提的是,添加TMAO后,2种降压过程处理的样品中,可培养细菌的平均丰度均没有明显提升,但样品中的可培养细菌种类明显提升,部分种属的丰度也发生了明显的变化。此外,一些种属仅在特定的压力和底物存在的条件下出现。【结论】不同的降压方式能够影响深海海水中可培养细菌的丰度和群落组成,添加TMAO的富集实验表明可以增加分离到的细菌的种类,为下一步的深入研究提供良好的研究基础。     

Discovering structure in multiple outcomes models for tests of childhood neurodevelopment

Bayesian model–based clustering provides a powerful and flexible tool that can be incorporated into regression models to better understand the grouping of observations. Using data from the Seychelles Child Development Study, we explore the effect of prenatal methylmercury exposure on 20 neurodevelopmental outcomes measured in 9-year-old children. Rather than cluster individual subjects, we cluster the outcomes within a multiple outcomes model. By using information in the data to nest the outcomes into groups called domains, the model more accurately reflects the shared characteristics of neurodevelopmental domains and improves estimation of the overall and outcome-specific exposure effects by shrinking effects within and between domains selected by the data. The Bayesian paradigm allows for sampling from the posterior distribution of the grouping parameters; thus, inference can be made about group membership and their defining characteristics. We avoid the often difficult and highly subjective requirement of a priori identification of the total number of groups by incorporating a Dirichlet process prior to form a fully Bayesian multiple outcomes model.     

Magic bullets and golden rules: data sampling in molecular phylogenetics

Data collection for molecular phylogenetic studies is based on samples of both genes and taxa. In an ideal world, with no limitations to resources, as many genes could be sampled as deemed necessary to address phylogenetic problems. Given limited resources in the real world, inadequate (in terms of choice of genes or number of genes) sequences or restricted taxon sampling can adversely affect the reliability or information gained in phylogenetics. Recent empirical and simulation-based studies of data sampling in molecular phylogenetics have reached differing conclusions on how to deal with these problems. Some advocated sampling more genes, others more taxa. There is certainly no ‘magic bullet’ that will fit all phylogenetic problems, and no specific ‘golden rules’ have been deduced, other than that single genes may not always contain sufficient phylogenetic information. However, several general conclusions and suggestions can be made. One suggestion is that the determination of a multiple, but moderate number (e.g., 6–10) of gene sequences might take precedence over sequencing a larger set of genes and thereby permit the sampling of more taxa for a phylogenetic study.     

A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

Summary .  We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike's information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.     

A model of weak selection in the infinite alleles framework

Ewens (1972) proposed a model in the infinite allele framework for populations with neutrality of all alleles at a particular locus. This paper proposes a generalisation of Ewens' result for situations where there is a form of weak selection. The models considered here are continuous time, discrete state space Markov processes.