Hierarchical Archimedean copula models for the analysis of binary familial data

Archimedean copulas are commonly used in a wide range of statistical models due to their simplicity, manageable analytical expressions, rich choices of generator functions, and other workable properties. However, the exchangeable dependence structure inherent to Archimedean copulas limits its application to familial data, where the dependence among family members is often different. When response variables are binary, modeling the familial associations becomes more challenging due to the stringent constraints imposed on the dependence parameters. This paper proposes hierarchical Archimedean copulas to account for the natural hierarchical dependence structure in familial data and addresses the details in the modeling of binary familial data and the inference based on maximum likelihood estimate. An example showing the flexibility of this powerful tool is also presented with possible extension to other similar studies.     

Functional interaction–based nonlinear models with application to multiplatform genomics data

Functional regression allows for a scalar response to be dependent on a functional predictor; however, not much work has been done when a scalar exposure that interacts with the functional covariate is introduced. In this paper, we present 2 functional regression models that account for this interaction and propose 2 novel estimation procedures for the parameters in these models. These estimation methods allow for a noisy and/or sparsely observed functional covariate and are easily extended to generalized exponential family responses. We compute standard errors of our estimators, which allows for further statistical inference and hypothesis testing. We compare the performance of the proposed estimators to each other and to one found in the literature via simulation and demonstrate our methods using a real data example.     

Improving estimation and prediction in linear regression incorporating external information from an established reduced model

We consider a situation where there is rich historical data available for the coefficients and their standard errors in a linear regression model describing the association between a continuous outcome variable Y and a set of predicting factors X , from a large study. We would like to use this summary information for improving inference in an expanded model of interest, Y given X , B . The additional variable B is a new biomarker, measured on a small number of subjects in a new dataset. We formulate the problem in an inferential framework where the historical information is translated in terms of nonlinear constraints on the parameter space and propose both frequentist and Bayes solutions to this problem. We show that a Bayesian transformation approach proposed by Gunn and Dunson is a simple and effective computational method to conduct approximate Bayesian inference for this constrained parameter problem. The simulation results comparing these methods indicate that historical information on E( Y | X ) can improve the efficiency of estimation and enhance the predictive power in the regression model of interest E( Y | X , B ). We illustrate our methodology by enhancing a published prediction model for bone lead levels in terms of blood lead and other covariates, with a new biomarker defined through a genetic risk score.     

A comparison of seven random‐effects models for meta‐analyses that estimate the summary odds ratio

Comparative trials that report binary outcome data are commonly pooled in systematic reviews and meta‐analyses. This type of data can be presented as a series of 2‐by‐2 tables. The pooled odds ratio is often presented as the outcome of primary interest in the resulting meta‐analysis. We examine the use of 7 models for random‐effects meta‐analyses that have been proposed for this purpose. The first of these models is the conventional one that uses normal within‐study approximations and a 2‐stage approach. The other models are generalised linear mixed models that perform the analysis in 1 stage and have the potential to provide more accurate inference. We explore the implications of using these 7 models in the context of a Cochrane Review, and we also perform a simulation study. We conclude that generalised linear mixed models can result in better statistical inference than the conventional 2‐stage approach but also that this type of model presents issues and difficulties. These challenges include more demanding numerical methods and determining the best way to model study specific baseline risks. One possible approach for analysts is to specify a primary model prior to performing the systematic review but also to present the results using other models in a sensitivity analysis. Only one of the models that we investigate is found to perform poorly so that any of the other models could be considered for either the primary or the sensitivity analysis.     

The impact of immunization programs on 10 vaccine preventable diseases in Italy: 1900–2015

BackgroundVaccination has determined a dramatic decline in morbidity and mortality from infectious diseases over the last century. However, low perceived risk of the infectious threat and increased concern about vaccines’ safety led to a reduction in vaccine coverage, with increased risk of disease outbreaks.MethodsAnnual surveillance data of nationally communicable infectious diseases in Italy between 1900 and 2015 were used to derive trends in morbidity and mortality rates before and after vaccine introduction, focusing particularly on the effect of vaccination programs. Autoregressive integrated moving average models were applied to ten vaccine-preventable diseases: diphtheria, tetanus, poliomyelitis, hepatitis B, pertussis, measles, mumps, rubella, chickenpox, and invasive meningococcal disease. Results of these models referring to data before the immunization programs were projected on the vaccination period to estimate expected cases. The difference between observed and projected cases provided estimates of cases avoided by vaccination.ResultsThe temporal trend for each disease started with high incidence rates, followed by a period of persisting reduction. After vaccine introduction, and particularly after the recommendation for universal use among children, the current rates were much lower than those forecasted without vaccination, both in the whole population and among the 0-to-4 year olds, which is, generally, the most susceptible age class. Assuming that the difference between incidence rates before and after vaccination programs was attributable only to vaccine, more than 4 million cases were prevented, and nearly 35% of them among children in the early years of life. Diphtheria was the disease with the highest number of prevented cases, followed by mumps, chickenpox and measles.ConclusionsUniversal vaccination programs represent the most effective prevention tool against infectious diseases, having a major impact on human health. Health authorities should make any effort to strengthen public confidence in vaccines, highlighting scientific evidence of vaccination benefits.     

兔颞下颌关节盘移位与骨关节病关系的评价

目的评价颞下颌关节盘移位与骨关节病的关系。方法通过手术方法将２５只大耳白兔的颞下颌关节盘前移，术后１、２、４、８、１２周进行组织病理学检查。结果１１侧手术关节为部分关节盘前移位，８侧为完全性关节盘前移位，６侧为关节盘穿孔。部分关节盘前移位出现早期退行性改变和后期生理性改建，完全性关节盘前移位和关节盘穿孔出现退行性改变和严重的骨关节病。结论颞下颌关节盘前移位的程度与骨关节病有关，关节盘前移位越明显，越易出现骨关节病     

Morphogenesis and bone integration of the mouse mandibular third molar

The mouse third molar (M3) develops postnatally and is thus a unique model for studying the integration of a non-mineralized tooth with mineralized bone. This study assessed the morphogenesis of the mouse M3, related to the alveolar bone, comparing M3 development with that of the first molar (M1), the most common model in odontogenesis. The mandibular M3 was evaluated from initiation to eruption by morphology and by assessing patterns of proliferation, apoptosis, osteoclast distribution, and gene expression. Three-dimensional reconstruction and explant cultures were also used. Initiation of M3 occurred perinatally, as an extension of the second molar (M2) which grew into a region of soft mesenchymal tissue above the M2, still far away from the alveolar bone. The bone-free M3 bud gradually became encapsulated by bone at the cap stage at postnatal day 3. Osteoclasts were first visible at postnatal day 4 when the M3 came into close contact with the bone. The number of osteoclasts increased from postnatal day 8 to postnatal day 12 to form a space for the growing tooth. The M3 had erupted by postnatal day 26. The M3, although smaller than the M1, passed through the same developmental stages over a similar time span but showed differences in initiation and in the timing of bone encapsulation.     

Competitive adsorption versus surface complexation as models for the simultaneous adsorption of metal complexes and free ligands

A statistical thermodynamical approach to the study of anion-induced adsorption of Cd(II) from halide solutions is presented. The simultaneous adsorption of metal complex and ligand is introduced in the isotherms by considering two possible mechanisms — competitive adsorption and surface complexation. These isotherms have been tested for the system Cd(II) in KBr at several ionic strengths. The experimental surface excesses of Cd(II) calculated from single-step chronocoulometry can be simulated, giving an explanation for the desorption of the metal complex at positive potentials. Also, the change in ligand adsorption promoted by the adsorption of the metal complex has been calculated. Both approaches lead to the conclusion that the anionic tricoordinate metal complex CdBr₃^? and the tetracoordinate CdBr₄^2? are the absorbed species on the electrode surface, with CdBr₄^2? dominating at higher bromide concentrations.     

Statistical Methodologies Useful for the Analysis of Data from Risk-assessment Studies

In dentistry, the vast majority of studies attempting to predict who is at high risk for getting a disease or condition, or attempting to identify risk factors for a specific disease or condition, have focused either on only one risk factor at a time, or have measured multiple potential risk factors, but analyzed their effects in isolation. Since researchers tend to agree that most dental conditions have a multiple etiology, it is necessary to develop models that consider simultaneously the effect of a number of potential risk factors on the disease or condition of interest if we are to have any understanding of the relative impact of potential risk factors. Many existing statistical techniques will aid dental researchers in identifying risk factors. However, the selection of an appropriate analytic technique depends on a number of conditions. The strategy for this paper is to discuss a wide range of possible statistical techniques that may be applied to the problem of deriving a model for identification of multiple risk factors for dental diseases and conditions. We have approached this task by presenting a number of dental research problems needing an appropriate analytic technique. Next, basic issues that must be considered in choosing an appropriate analytic strategy are discussed. These issues include features of the study design, the data structure of the variables being measured, and the types of assumptions that are applicable to provide valid inferences about the target population of interest. A matrix of possible analytic techniques is presented for various combinations of study-design and data-structure features. After a discussion of each of the techniques, the appropriate statistical techniques for each of the dental examples are described. The issues and examples presented in this paper should be of use to dental researchers who wish to investigate multiple risk factors for a disease or condition of interest.     

口腔种植学虚拟现实三维模型素材库的初步建立

目的:建立口腔种植修复学教学用虚拟现实所需要的数字化三维模型库及相关素材,为制作三维互动化的虚拟现实教学课件提供基础条件。方法:利用现代计算机三维图形技术,在3Dsmax和Rhinoceros三维造型软件中,根据口腔种植学相关实体的三维形态,构建相应数字化三维模型,并结合平面图形软件制作模型贴图和材质。搭建1例口腔种植学典型三维虚拟场景,初步利用虚拟现实软件生成可执行的三维互动教学课件。结果:制作完成了部分常用口腔种植系统的相关三维模型,初步建立起了口腔种植修复学教学用虚拟现实的三维模型素材库,初步搭建了1例典型的口腔种植修复临床治疗场景,编辑生成了可直接操作的三维虚拟现实课件。结论:虚拟现实技术在口腔种植修复学教学中将会有广泛的应用,制作基于个人电脑的虚拟现实教学课件对口腔医学教学也有着较高的实用价值。三维模型素材库的初步建立为开发完善的口腔医学虚拟现实教学系统提供了先期研究基础和所必需的基本素材。