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Sequential Experimental Designs for Generalized Linear Models

广义线性模型的序贯实验设计

We consider the problem of experimental design when the response is modeled by a generalized linear model (GLM) and the experimental plan can be determined sequentially. Most previous research on this problem has been limited either to one-factor, binary response experiments or to augmenting the design when there are already sufficient data to compute parameter estimates. We suggest a new procedure for the sequential choice of observations that offers five important…

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Mixtures of g Priors for Bayesian Variable Selection

用于贝叶斯变量选择的 g 先验混合

Zellner's g prior remains a popular conventional prior for use in Bayesian variable selection, despite several undesirable consistency issues. In this article we study mixtures of g priors as an alternative to default g priors that resolve many of the problems with the original formulation while maintaining the computational tractability that has made the g prior so popular. We present theoretical properties of the mixture g priors and provide real and simulated examples to…

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Aberrant Effects of Treatment

治疗的异常效应

Often, the protocol for a randomized controlled trial states that if a patient's health deteriorates substantially in certain specific ways, then the study treatments will no longer be considered adequate medical care, and the patient will be taken off the study treatments and given the needed care. Aberrant responses of this sort present special problems of interpretation. The few such patients may have received only limited exposure to the study treatments, but also may…

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Bayesian Hierarchical Curve Registration

贝叶斯层级曲线配准

Functional data often exhibit a common shape, but with variations in amplitude and phase across curves. The analysis often proceeds by synchronization of the data through curve registration. In this article we propose a Bayesian hierarchical model for curve registration. Our hierarchical model provides a formal account of amplitude and phase variability while borrowing strength from the data across curves in the estimation of the model parameters. We discuss extensions of…

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Randomization Inference in a Group–Randomized Trial of Treatments for Depression

抑郁症治疗整群随机试验中的随机化推断

In the Prospect Study, in 10 pairs of two primary-care practices, one practice was picked at random to receive a “depression care manager” to treat its depressed patients. Randomization inference, properly performed, reflects the assignment of practices, not patients, to treatment or control. Yet, pertinent data describe individual patients: depression outcomes, baseline covariates, compliance with treatment. The methods discussed use only (i) the random assignment of…

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Spatial-Temporal Modeling of Forest Gaps Generated by Colonization From Below- and Above-Ground Bark Beetle Species

地下与地上树皮甲虫定殖引致林隙的时空建模

Studies of forest declines are important, because they both reduce timber production and affect successional trajectories of landscapes and ecosystems. Of particular interest is the decline of red pines, which is characterized by expanding areas of dead and chlorotic trees in plantations throughout the Great Lakes region. Here we examine the impact of two bark beetle groups, red turpentine beetles and pine engraver bark beetles, on tree mortality and the subsequent gap…

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Application of Multidimensional Selective Item Response Regression Model for Studying Multiple Gene Methylation in SV40 Oncogenic Pathways

用多维选择性项目反应回归研究 SV40 致癌通路中的多基因甲基化

Alteration of gene methylation patterns has been reported to be involved in the early onsets of many human malignancies. Many exogenous risk factors, such as cigarette smoke, dietary additives, chemical exposures, radiation, and biologic agents including viral infection, are involved in the methylation pathways of cancers. We propose a multidimensional selective item response regression model to describe and test how a risk factor may alter molecular pathways involving…

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Relating Ambient Particulate Matter Concentration Levels to Mortality Using an Exposure Simulator

利用暴露模拟器研究环境颗粒物浓度与死亡率的关系

Since the U.S. Environmental Protection Agency began widespread monitoring of PM2.5 (particulate matter <2.5 μ in diameter) concentration levels in the late 1990s, the epidemiological community has performed several observational studies directly relating PM2.5 concentration to various health endpoints including mortality and morbidity. However, recent research suggests that human exposure to the constituents of PM2.5 may differ significantly from ambient (or outdoor) PM2.5…

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A Capture–Recapture Approach for Screening Using Two Diagnostic Tests With Availability of Disease Status for the Test Positives Only

仅核实筛查阳性者疾病状态时的双诊断检验捕获—再捕获方法

The article considers screening human populations with two screening tests. If any of the two tests is positive, then full evaluation of the disease status is undertaken; however, if both diagnostic tests are negative, then disease status remains unknown. This procedure leads to a data constellation in which, for each disease status, the 2 × 2 table associated with the two diagnostic tests used in screening has exactly one empty, unknown cell. To estimate the unobserved cell…

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Bayesian Clustering of Transcription Factor Binding Motifs

转录因子结合基序的贝叶斯聚类

Genes are often regulated in living cells by proteins called transcription factors that bind directly to short segments of DNA in close proximity to specific genes. These binding sites have a conserved nucleotide appearance, which is called a motif. Several recent studies of transcriptional regulation require the reduction of a large collection of motifs into clusters based on the similarity of their nucleotide composition. We present a principled approach to this clustering…

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Modeling Disease Progression With Longitudinal Markers

利用纵向标志物建模疾病进展

In this paper we propose a Bayesian natural history model for disease progression based on the joint modeling of longitudinal biomarker levels, age at clinical detection of disease and disease status at diagnosis. We establish a link between the longitudinal responses and the natural history of the disease by using an underlying latent disease process which describes the onset of the disease and models the transition to an advanced stage of the disease as dependent on the…

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Principal Stratification for Causal Inference With Extended Partial Compliance

扩展部分依从情形下因果推断的主层化方法

Many double-blind placebo-controlled randomized experiments with active drugs suffer from complications beyond simple noncompliance. First, the compliance with assigned dose is often partial, with patients taking only part of the assigned dose, whether active or placebo. Second, the blinding may be imperfect in the sense that there may be detectable positive or negative side effects of the active drug, and consequently, simple compliance has to be extended to allow different…

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On Estimating Diagnostic Accuracy From Studies With Multiple Raters and Partial Gold Standard Evaluation

多评定者与部分金标准核验下的诊断准确性估计

We are often interested in estimating sensitivity and specificity of a group of raters or a set of new diagnostic tests in situations in which gold standard evaluation is expensive or invasive. Numerous authors have proposed latent modeling approaches for estimating diagnostic error without a gold standard. Albert and Dodd showed that, when modeling without a gold standard, estimates of diagnostic error can be biased when the dependence structure between tests is…

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Estimating Flight Departure Delay Distributions—A Statistical Approach With Long-Term Trend and Short-Term Pattern

结合长期趋势与短期模式的航班离港延误分布估计

In this article we develop a model for estimating flight departure delay distributions required by air traffic congestion prediction models. We identify and study major factors that influence flight departure delays, and develop a strategic departure delay prediction model. This model employs nonparametric methods for daily and seasonal trends. In addition, the model uses a mixture distribution to estimate the residual errors. To overcome problems with local optima in the…

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A Mixture Model With Dependent Observations for the Analysis of CSFE–Labeling Experiments

用于 CFSE 标记实验分析的相关观测混合模型

Recent advances in flow cytometry have resulted in the development of powerful bioassays to analyze the proliferation of cell populations. The carboxy-fluorescein diacetate succinimidyl ester (CFSE)-labeling experiment is one such assay that is widely used to study cell kinetics and as a standard tool for investigating lymphocyte proliferation. Several mathematical models have been proposed to describe cell proliferation during CFSE experiments. The statistical analysis of…

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Joint Modeling of Time Series Measures and Recurrent Events and Analysis of the Effects of Air Quality on Respiratory Symptoms

时间序列测量与复发事件的联合建模:空气质量与呼吸症状

Exposure to ambient pollutants at concentrations above defined standards is a risk factor for respiratory symptoms, especially in sensitive children. Many studies have been undertaken to monitor air quality and to assess its association with respiratory symptoms. We propose a joint mixed-effects regression model of time series measures and recurrent events to analyze the air quality and respiratory symptom data from the Yale Mothers and Infants Health Study. Three mothers'…

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An Approach to Multivariate Covariate-Dependent Quantile Contours With Application to Bivariate Conditional Growth Charts

协变量依赖的多元分位数轮廓与二元条件生长图

Multivariate quantile contours are useful in numerous applications and have been studied in different contexts. However, no easy solutions exist when dynamic and conditional quantile contours are needed without strong distributional assumptions. In this article we propose a new form of bivariate quantile contours and a two-stage estimation procedure to take time effect into account. The proposed procedure relies on quantile regression for longitudinal data and is flexible to…

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Efficient Local Estimation for Time-Varying Coefficients in Deterministic Dynamic Models With Applications to HIV-1 Dynamics

确定性动态模型中时变系数的高效局部估计及 HIV-1 应用

Recently deterministic dynamic models have become very popular in biomedical research and other scientific areas; examples include modeling human immunodeficiency virus (HIV) dynamics, pharmacokinetic/pharmacodynamic analysis, tumor cell kinetics, and genetic network modeling. In this article we propose estimation methods for the time-varying coefficients in deterministic dynamic systems that are usually described by a set of differential equations. Three two-stage local…

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Goodness of Fit of Social Network Models

社会网络模型的拟合优度

We present a systematic examination of a real network data set using maximum likelihood estimation for exponential random graph models as well as new procedures to evaluate how well the models fit the observed networks. These procedures compare structural statistics of the observed network with the corresponding statistics on networks simulated from the fitted model. We apply this approach to the study of friendship relations among high school students from the National…

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