Analysis of the Bovine Spongiform Encephalopathy Maternal Cohort Study: Evidence for Direct Maternal Transmission

In an initial exploratory analysis of the bovine spongiform encephalopathy (BSE) maternal cohort study data we demonstrate several confounding effects in the study design. Given these effects, we assess a variety of statistical models to determine the relative contributions of direct maternal transmission of the aetiological agent of BSE and of genetic susceptibility to the observed maternally enhanced risk of BSE in the offspring of affected dams. To control for the substantial between-herd variation in the risk of exposure to the BSE agent, it is essential that analyses take into account the matched pair structure of the data. Maternal exposure is estimated to be most important in animals born within 150 days of disease onset in their dams. The analysis of a full survival likelihood model indicates that the hypothesis of maternal transmission with no genetic variation in susceptibility fits the data significantly better than the hypothesis of genetically variable susceptibility with no maternal transmission. However, models incorporating both maternal transmission and genetically variable susceptibility fit the data significantly better than pure maternal transmission models. Although genetic susceptibility cannot be excluded as the cause of the cohort study results in the absence of detailed genotyping, the analysis of these study data suggest that low level maternal transmission of BSE is, at least in part, responsible for the significantly enhanced risk of BSE in the offspring of affected dams. Similar results indicating significant maternal transmission in the later stages of the dam incubation period are obtained from the independent analysis of data on the dam–offspring relationships among confirmed BSE cases.     

A semiparametric method of multiple imputation

In this paper, we describe how to use multiple imputation semiparametrically to obtain estimates of parameters and their standard errors when some individuals have missing data. The methods given require the investigator to know or be able to estimate the process generating the missing data but requires no full distributional form for the data. The method is especially useful for non-standard problems, such as estimating the median when data are missing.     

A stochastic approximation algorithm for maximum-likelihood estimation with incomplete data

We propose a new stochastic approximation (SA) algorithm for maximum-likelihood estimation (MLE) in the incomplete-data setting. This algorithm is most useful for problems when the EM algorithm is not possible due to an intractable E-step or M-step. Compared to other algorithm that have been proposed for intractable EM problems, such as the MCEM algorithm of Wei and Tanner (1990), our proposed algorithm appears more generally applicable and efficient. The approach we adopt is inspired by the Robbins-Monro (1951) stochastic approximation procedure, and we show that the proposed algorithm can be used to solve some of the long-standing problems in computing an MLE with incomplete data. We prove that in general O(n) simulation steps are required in computing the MLE with the SA algorithm and O(n log n) simulation steps are required in computing the MLE using the MCEM and/or the MCNR algorithm, where n is the sample size of the observations. Examples include computing the MLE in the nonlinear error-in-variable model and nonlinear regression model with random effects.     

我国统计数据质量的评估方法研究————————趋势模拟评估法及其应用

 本文采用组合模型的形式对时间序列数据的变化特点建模，在模型通过各种检验、具有良好统计预测功能的基础上，从检验异常值的角度来分析预测值与实际值之间差异的程度，找出离群数据，利用数理统计中检验实验观测数据异常值的方法，对离群数据的误差进行统计上的显著检验，从而评估统计数据的质量。文章以我国国内生产总值（GDP）为研究对象，选取我国1978-2003年间的GDP作为样本，运用趋势模拟评估法来评估我国2004年国内生产总值的准确性。对我国经济指标的时间序列数据进行了实证分析。     

Modeling for Risk Assessment of Neurotoxic Effects

The regulation of noncancer toxicants, including neurotoxicants, has usually been based upon a reference dose (allowable daily intake). A reference dose is obtained by dividing a no-observed-effect level by uncertainty (safety) factors to account for intraspecies and interspecies sensitivities to a chemical. It is assumed that the risk at the reference dose is negligible, but no attempt generally is made to estimate the risk at the reference dose. A procedure is outlined that provides estimates of risk as a function of dose. The first step is to establish a mathematical relationship between a biological effect and the dose of a chemical. Knowledge of biological mechanisms and/or pharmacokinetics can assist in the choice of plausible mathematical models. The mathematical model provides estimates of average responses as a function of dose. Secondly, estimates of risk require selection of a distribution of individual responses about the average response given by the mathematical model. In the case of a normal or lognormal distribution, only an estimate of the standard deviation is needed. The third step is to define an adverse level for a response so that the probability (risk) of exceeding that level can be estimated as a function of dose. Because a firm response level often cannot be established at which adverse biological effects occur, it may be necessary to at least establish an abnormal response level that only a small proportion of individuals would exceed in an unexposed group. That is, if a normal range of responses can be established, then the probability (risk) of abnormal responses can be estimated. In order to illustrate this process, measures of the neurotransmitter serotonin and its metabolite 5-hydroxyindoleacetic acid in specific areas of the brain of rats and monkeys are analyzed after exposure to the neurotoxicant methylene-dioxymethamphetamine. These risk estimates are compared with risk estimates from the quantal approach in which animals are classified as either abnormal or not depending upon abnormal serotonin levels.     

Multilevel models for repeated binary outcomes: attitudes and voting over the electoral cycle

Models for fitting longitudinal binary responses are explored by using a panel study of voting intentions. A standard multilevel repeated measures logistic model is shown to be inadequate owing to a substantial proportion of respondents who maintain a constant response over time. A multivariate binary response model is shown to be a better fit to the data.     

The Effect of Drop-Out on the Efficiency of Longitudinal Experiments

It is shown that drop-out often reduces the efficiency of longitudinal experiments considerably. In the framework of linear mixed models, a general, computationally simple method is provided, for designing longitudinal studies when drop-out is to be expected, such that there is little risk of large losses of efficiency due to the missing data. All the results are extensively illustrated using data from a randomized experiment with rats.     

高校图书馆网络资源共享面临的困境及对策

目前网络资源共享面临着认识、管理体制、网络操作人没专业化等困境,对此,我们应采取以下一些措施树立正确观念;培训专业技术人才;开展网上教育;建立网络协调机构;规范网络数据标准。同时我们还应加强各高校特色数据库建设,丰富网上信息资源,以促使我国网络资源共享进一发展。     

Clustering for contingency tables: boxes and partitions

The correspondence analysis (CA) method appears to be an effective tool for analysis of interrelations between rows and columns in two-way contingency data. A discrete version of the method, box clustering, is developed in the paper using an approximation version of the CA model extended to the case when CA factor values are required to be Boolean. Several properties of the proposed SEFIT-BOX algorithm are proved to facilitate interpretation of its output. It is also shown that two known partitioning algorithms (applied within row or column sets only) could be considered as locally optimal algorithms for fitting the model, and extensions of these algorithms to a simultaneous row and column partitioning problem are proposed.     

A metadata approach to statistical query processing

Concerning the task of integrating census and survey data from different sources as it is carried out by supranational statistical agencies, a formal metadata approach is investigated which supports data integration and table processing simultaneously. To this end, a metadata model is devised such that statistical query processing is accomplished by means of symbolic reasoning on machine-readable, operative metadata. As in databases, statistical queries are stated as formal expressions specifying declaratively what the intended output is; the operations necessary to retrieve appropriate available source data and to aggregate source data into the requested macrodata are derived mechanically. Using simple mathematics, this paper focuses particularly on the metadata model devised to harmonize semantically related data sources as well as the table model providing the principal data structure of the proposed system. Only an outline of the general design of a statistical information system based on the proposed metadata model is given and the state of development is summarized briefly.