Optimal adaptive designs for acute oral toxicity assessment

Acute oral toxicity studies are used to assess the toxicity to experimental animals of a single dose of the substance under investigation, assigning the substance to one of a number of classes. Animal welfare concerns have led to the development of three adaptive designs as alternatives to the traditional fixed sample design. These lead to reductions in the number of animals required in total and in the number exposed to lethal doses. In this paper, we show how designs can be constructed to optimise utility functions combining the need to classify correctly with the desire to use a small number of animals. Constrained optimal designs are also obtained in which no animal is exposed to a dose higher than that at which a death has been observed. The optimal designs lead to the correct classification with high probability whilst reducing the expected number of animal deaths relative to existing adaptive designs.     

On implementing Jeffreys' substitution likelihood for Bayesian inference concerning the medians of unknown distributions

When statisticians are uncertain as to which parametric statistical model to use to analyse experimental data, they will often resort to a non-parametric approach. The purpose of this paper is to provide insight into a simple approach to take when it is unclear as to the appropriate parametric model and plan to conduct a Bayesian analysis. I introduce an approximate, or substitution likelihood, first proposed by Harold Jeffreys in 1939 and show how to implement the approach combined with both a non-informative and an informative prior to provide a random sample from the posterior distribution of the median of the unknown distribution. The first example I use to demonstrate the approach is a within-patient bioequivalence design and then show how to extend the approach to a parallel group design.     

Estimation and Testing of Learning Curves

This article describes a new approach to learning curve estimation. Our approach is to formulate statistical procedures that conform to alternative learning curve theories. This leads to the development of nonlinear statistical models of the learning curves. For the three data sets analyzed, autocorrelation seems to be an important problem. Parameter estimates were derived using the maximum likelihood principle in the presence of first-order autocorrelation. Nonnested tests were used to select the appropriate formulation of the learning curve. Research conclusions are to use unit data when estimating a learning curve and to be prepared to treat autocorrelation if present.     

A Bayesian model for estimating the malaria transition probabilities considering individuals lost to follow-up

It is known that patients may cease participating in a longitudinal study and become lost to follow-up. The objective of this article is to present a Bayesian model to estimate the malaria transition probabilities considering individuals lost to follow-up. We consider a homogeneous population, and it is assumed that the considered period of time is small enough to avoid two or more transitions from one state of health to another. The proposed model is based on a Gibbs sampling algorithm that uses information of lost to follow-up at the end of the longitudinal study. To simulate the unknown number of individuals with positive and negative states of malaria at the end of the study and lost to follow-up, two latent variables were introduced in the model. We used a real data set and a simulated data to illustrate the application of the methodology. The proposed model showed a good fit to these data sets, and the algorithm did not show problems of convergence or lack of identifiability. We conclude that the proposed model is a good alternative to estimate probabilities of transitions from one state of health to the other in studies with low adherence to follow-up.     

Prediction for generalized linear models

Regression models are often used to make predictions. All the information needed is contained in the predictive distribution. However, this cannot be evaluated explicitly for most generalized linear models. We construct two approximations to this distribution and demonstrate their use on two sets of survival data, corresponding to the outcome of patients admitted to intensive care units and the survival times of leukaemia patients.Regression models are often used to make predictions. All the information needed is contained in the predictive distribution. However, this cannot be evaluated explicitly for most generalized linear models. We construct two approximations to this distribution and demonstrate their use on two sets of survival data, corresponding to the outcome of patients admitted to intensive care units and the survival times of leukaemia patients.Regression models are often used to make predictions. All the information needed is contained in the predictive distribution. However, this cannot be evaluated explicitly for most generalized linear models. We construct two approximations to this distribution and demonstrate their use on two sets of survival data, corresponding to the outcome of patients admitted to intensive care units and the survival times of leukaemia patients.Regression models are often used to make predictions. All the information needed is contained in the predictive distribution. However, this cannot be evaluated explicitly for most generalized linear models. We construct two approximations to this distribution and demonstrate their use on two sets of survival data, corresponding to the outcome of patients admitted to intensive care units and the survival times of leukaemia patients.     

Misspecification of a binary dependent variable in the logistic model controlling for the repeated longitudinal measures

Many medical applications are interested to know the disease status. The disease status can be related to multiple serial measurements. Nevertheless, owing to various reasons, the binary outcome can be measured incorrectly. The estimators derived from the misspecified outcome can be biased. This paper derives the complete data likelihood function to incorporate both the multiple serial measurements and the misspecified outcome. Owing to the latent variables, EM algorithm is used to derive the maximum-likelihood estimators. Monte Carlo simulations are conducted to compare the impact of misspecification on the estimates. A retrospective data for the recurrence of atrial fibrillation is used to illustrate the usage of the proposed model.     

On a redundant repairable system with switching failure: Bayesian approach

System characteristics of a redundant repairable system are studied from a Bayesian viewpoint with different types of priors assumed for the unknown parameters. The system consists of two primary units, one standby unit, and one repair facility which is activated when switching to standby fails. Times to failure and times to repair of the operating units are assumed to follow exponential distributions. When time to failure and time to repair have uncertain parameters, a Bayesian approach is adopted to evaluate system characteristics. Monte Carlo simulation is used to derive the posterior distribution for the mean time to system failure and steady-state availability. Some numerical experiments are performed to illustrate the results derived in this paper.     

A response to M. Stone: 'How not to measure the efficiency of public services (and how one might)'

Summary.  This is a response to Stone's criticisms of the Spottiswoode report to the UK Treasury which was responding to the Treasury's request for improved methods to evaluate the efficiency and productivity of the 43 police districts in England and Wales. The Spottiswoode report recommended uses of data envelopment analysis (DEA) and stochastic frontier analysis (SFA), which Stone critiqued en route to proposing an alternative approach. Here we note some of the most serious errors in his criticism and inaccurate portrayals of DEA and SFA. Most of our attention is devoted to DEA, and to Stone's recommended alternative approach without much attention to SFA, partly because of his abbreviated discussion of the latter. In our response we attempt to be constructive as well as critical by showing how Stone's proposed approach can be joined to DEA to expand his proposal beyond limitations in his formulations.     

我国35个大中城市房价-持久收入比研究

房价收入比是反映居民购房可支付能力的重要指标。为了更真实准确反映我国居民长期承受的购房负担,本文基于持久收入假说,利用我国35个大中城市数据,建立状态空间模型,估计出持久收入意义下的房价收入比,通过情景分析确定其合理上限,并分析主要特征。研究表明：①我国房价-持久收入比的合理上限为7.6,2002-2013年35个大中城市的房价-持久收入比均值是9.2,其中28个城市已超过该上限,比值最高的北京已达到14.9;②城市越发达,则房价-持久收入比越高,居民需要承受的购房压力越大,且在时间趋势上,一线与二三线城市间的差距呈现扩大趋势;③在地理分布上,我国东部、中部、东北地区、西部大中城市的房价-持久收入比呈现从高到低的排列顺序;④相比于传统方法中根据可支配收入测算的房价收入比,房价-持久收入比与其约有10%的差异。当前我国政府应采取有效措施继续限制房价,减轻居民购房负担。     

Bayesian local influence for survival models

The aim of this paper is to develop a Bayesian local influence method (Zhu et al. 2009, submitted) for assessing minor perturbations to the prior, the sampling distribution, and individual observations in survival analysis. We introduce a perturbation model to characterize simultaneous (or individual) perturbations to the data, the prior distribution, and the sampling distribution. We construct a Bayesian perturbation manifold to the perturbation model and calculate its associated geometric quantities including the metric tensor to characterize the intrinsic structure of the perturbation model (or perturbation scheme). We develop local influence measures based on several objective functions to quantify the degree of various perturbations to statistical models. We carry out several simulation studies and analyze two real data sets to illustrate our Bayesian local influence method in detecting influential observations, and for characterizing the sensitivity to the prior distribution and hazard function.     

Sample size calculation for logrank test and prediction of number of events over time

We review and compare existing methods for sample size calculation based on the logrank statistic and recommend the method of Lakatos for its accuracy and flexibility in allowing time-dependent rates of event, loss to follow-up, and noncompliance. We extend the Lakatos method to allow a general follow-up scheme, to handle non-inferiority tests, and to predict the number of events over calendar time. We apply the Lakatos method to the simple nonproportional hazard situation of delayed treatment effect to facilitate the comparison of different weighting methods and to evaluate the performance of the maximum combination tests. We use simulation studies to confirm the validity of the Lakatos method and its extensions.     

Hypothesis testing for the dispersion parameter of the hyper-Poisson regression model

The Poisson distribution is widely used to deal with count data, however, it is unable to capture the dispersion problems. The hyper-Poisson distribution is a particular case of the extended Conway–Maxwell distribution which takes into account the dispersion phenomena of the count data. The main motivation to consider this model is the possibility to link the mean to the regressor variables in very natural way to solve testing problems. So, this paper will be focalized in the gradient statistics to detect dispersions and to compare with the classical likelihood ratio statistic. Two illustrative applications are considered.     

多种综合评价方法的优劣判断研究

一、评价方法优劣判据的提出对于多指标多对象的综合评价 ,人们已提出许多不同的综合评价方法。传统评价方法有综合评分法、标准化计分法和功效系数法 ;多目标决策评价方法有优序法、引进次序法、双基点法和投影法 ;现代评价方法有主成分分析法 ,因子分析法、熵值法、模糊评价法、灰色关联分析评价法和层次分析评价法等。这些方法的应用相当活跃。但由于各种评价方法对原始数据的处理、权数的确定、评价方法本身掌握的标准和计算方法的不同 ,使评价结果存在着差异。这就向我们提出一个问题 ,即如何对各种综合评价方法进行优劣判断。我们认为…     

城市基本公共服务面向农民工开放度测量研究

2016年1月起实施的《居住证暂行条例》从根本上摒弃了农民工享有城市基本公共服务的制度桎梏，但农民工实际所能享有的水平尚取决于城市政府开放其公共服务的程度，包括“承诺”服务的领域多少和所设置的约束“条件”高低。本文以各城市居住证管理制度文件为依据，运用全国流动人口调查数据，测量城市基本公共服务面向农民工群体的开放度。结果显示：（1）城市基本公共服务全部领域超过一半面向农民工开放，开放度0.5076。即便在排他性服务领域，也超过1/3面向农民工开放，开放度达到0.3569，农民工开始实质性享有城市基本公共服务，但离平等权利仍有距离；（2）开放度在区域间的分布表现为东部城市低于中部城市更低于西部城市、直辖市低于省会城市更低于其他地级市，珠三角、长三角和京津冀城市低于三大经济圈外城市。相对发达的城市开放度更低，反映出开放度高低主要取决于城市政府的开放意愿而非提供服务的能力；（3）开放限制主要来源于“条件”约束，城市政府在排他性服务领域的承诺度0.7703，承诺服务的领域超过3/4。但约束度达0.4502，城市政府只向其中54.98%能满足约束条件的农民工开放所承诺的服务，源自约束条件的限制占到开放限制的60%以上。城市政府遵循居住证制度规范面向外来人口统一“承诺”服务，但通过“条件”约束选择性地限制了农民工获得城市基本公共服务的机会，农民工平等权利的最终实现尚需要政策制度的系统性配套改革。     

Preserving the Scholarly Collection: An Examination of the Perpetual Access Clauses in the Texas A&M University Libraries' Major E-Journal Licenses

Librarians are urged to lobby for the inclusion of perpetual access clauses during license negotiations and to examine existing contracts to ensure that perpetual access provisions are clearly understood and achievable. The authors analyzed the perpetual access clauses in nineteen of the Texas A&M University Libraries' licenses for electronic journal packages to determine the exact provisions granted, and found that the Libraries do not really know what access to previously subscribed content will entail. This raises profound concerns about the Libraries' ability to provide meaningful perpetual access to previously subscribed content to its users in the current environment.     

Analysis of the importance of on-field covariates in the German Bundesliga

In modern football, various variables as, for example, the distance a team runs or its percentage of ball possession, are collected throughout a match. However, there is a lack of methods to make use of these on-field variables simultaneously and to connect them with the final result of the match. This paper considers data from the German Bundesliga season 2015/2016. The objective is to identify the on-field variables that are connected to the sportive success or failure of the single teams. An extended Bradley–Terry model for football matches is proposed that is able to take into account on-field covariates. Penalty terms are used to reduce the complexity of the model and to find clusters of teams with equal covariate effects. The model identifies the running distance to be the on-field covariate that is most strongly connected to the match outcome.     

大数据背景下非概率抽样的统计推断问题

利用大数据进行抽样,很多情况下抽样框的构造比较困难,使得抽取的样本属于非概率样本,难以将传统的抽样推断理论应用到非概率样本中,如何解决非概率抽样的统计推断问题,是大数据背景下抽样调查面临的严重挑战。本文提出了解决非概率抽样统计推断问题的基本思路：一是抽样方法,可以考虑基于样本匹配的样本选择、链接跟踪抽样方法等,使得到的非概率样本近似于概率样本,从而可采用概率样本的统计推断理论;二是权数的构造与调整,可以考虑基于伪设计、模型和倾向得分等方法得到类似于概率样本的基础权数;三是估计,可以考虑基于伪设计、模型和贝叶斯的混合概率估计。最后,以基于样本匹配的样本选择为例探讨了具体解决方法。     

Stochastic Modelling of Times to Temperature for Furnaces Supplying Titanium Blooms to a Rolling Mill at TIMET

In conjunction with TIMET at Waunarlwydd (Swansea, UK) a model has been developed that will optimise the scheduling of various blooms to their eight furnaces so as to minimise the time taken to roll these blooms into the finished mill products. This production scheduling model requires reliable data on times taken for the various furnaces that heat the slabs and blooms to reach the temperatures required for rolling. These times to temperature are stochastic in nature and this paper identifies the distributional form for these times using the generalised F distribution as a modelling framework. The times to temperature were found to be similarly distributed over all furnaces. The identified distributional forms were incorporated into the scheduling model to optimise a particular campaign that was run at TIMET Swansea. Amongst other conclusion it was found that, compared to the actual campaign, the model produced a schedule that reduced the makespan by some 35%.     

城市能源效率的PS-kNN分类综合评价

为了对区域能源效率做出客观评价,提出一种新的基于不可控因素的地级城市分类的能源效率分类比较方法,旨在研究能源效率的地区可比性问题。分类问题的讨论包括类别个数的确定和分类方法的选取;分类个数和分类原则的确定采用预测强度和基础因子相结合的办法,进一步采用k最近邻分类方法对其余不可控因子进行分类预测,以避免所谓的自评判问题;运用文中所给出的综合分类结果对一些城市的能源效率进行评价,便于相关城市找到提高能源效率的有效措施。"     

SIMEX method for censored quantile regression with measurement error

Censored quantile regression serves as an important supplement to the Cox proportional hazards model in survival analysis. In addition to being exposed to censoring, some covariates may subject to measurement error. This leads to substantially biased estimate without taking this error into account. The SIMulation-EXtrapolation (SIMEX) method is an effective tool to handle the measurement error issue. We extend the SIMEX approach to the censored quantile regression with covariate measurement error. The algorithm is assessed via extensive simulations. A lung cancer study is analyzed to verify the validation of the proposed method.