Adaptive designs for single‐arm phase II trials in oncology

Clinical phase II trials in oncology are conducted to determine whether the activity of a new anticancer treatment is promising enough to merit further investigation. Two‐stage designs are commonly used for this situation to allow for early termination. Designs proposed in the literature so far have the common drawback that the sample sizes for the two stages have to be specified in the protocol and have to be adhered to strictly during the course of the trial. As a consequence, designs that allow a higher extent of flexibility are desirable. In this article, we propose a new adaptive method that allows an arbitrary modification of the sample size of the second stage using the results of the interim analysis or external information while controlling the type I error rate. If the sample size is not changed during the trial, the proposed design shows very similar characteristics to the optimal two‐stage design proposed by Chang et al. (Biometrics 1987; 43:865–874). However, the new design allows the use of mid‐course information for the planning of the second stage, thus meeting practical requirements when performing clinical phase II trials in oncology. Copyright © 2012 John Wiley & Sons, Ltd.     

Symmetrical design for symmetrical global sensitivity analysis of model output

Symmetrical global sensitivity analysis (SGSA) can help practitioners focusing on the symmetrical terms of inputs whose uncertainties have an impact on the model output, which allows reducing the complexity of the model. However, there remains the challenging problem of finding an efficient method to get symmetrical global sensitivity indices (SGSI) when the functional form of the symmetrical terms is unknown, including numerical and non-parametric situations. In this study, we propose a novel sampling plan, called symmetrical design, for SGSA. As a preliminary experiment for model feature extracting, such plan offers the virtue of run-size economy due to its closure respective to the given group. Using the design, we give estimation methods of SGSI as well as their asymptotic properties respectively for numerical model and non-parametrical model directly by the model outputs, and further propose a significance test for SGSI in non-parametric situation. A case study for a benchmark of GSA and a real data analysis show the effectiveness of the proposed design.     

服装系列设计方法之“抽象方程”论

通过长期的实践教学经验总结,将服装系列设计方法与数学方程联系在一起,形成服装系列设计的"抽象方程"理论,使服装系列设计具有了定向、定量、明确、有序的特征,力求寻找一条由简到繁、由易到难、循序渐进的学习实践方法,帮助服装设计初学者尽早摆脱设计瓶颈,步入设计正轨。     

Bayesian optimal sequential design for nonparametric regression via inhomogeneous evolutionary MCMC

We develop a novel computational methodology for Bayesian optimal sequential design for nonparametric regression. This computational methodology, that we call inhomogeneous evolutionary Markov chain Monte Carlo, combines ideas of simulated annealing, genetic or evolutionary algorithms, and Markov chain Monte Carlo. Our framework allows optimality criteria with general utility functions and general classes of priors for the underlying regression function. We illustrate the usefulness of our novel methodology with applications to experimental design for nonparametric function estimation using Gaussian process priors and free-knot cubic splines priors.     

期待“全考古学”时代的到来——兼评《牛河梁红山文化遗址建筑设计思想研究》

由于考古学产生约两百年来一直被定义为研究“物质遗存”，同时又缺乏探讨遗迹、器物纹饰内涵的理论与方法，导致考古学在基本理论、方法上与其目标追求长期陷入困境与悖论，而浩如烟海的纹饰及一些遗迹的内涵始终处于考古学的边缘不得解读。故基本教义的考古学从理论、方法到实践都是残缺不全的。因此提出“全考古学”概念，主张将考古学研究的对象定义为“文化遗存”，同时将考古学研究划分为三个阶段：第一阶段“田野考古”及资料整理；第二阶段“室内考古”即读懂材料；第三阶段，探究社会历史发展规律等。“全考古学”在研究方法上是全方位的、立体交叉式的；当前，我们应该大力倡导和支持“全考古学”研究。董婕、朱成杰著《牛河梁红山文化遗址建筑设计思想研究》正是“全考古学”第二阶段的典型作品。     

A revisit to test the equality of variances of several populations

We revisit the problem of testing homoscedasticity (or, equality of variances) of several normal populations which has applications in many statistical analyses, including design of experiments. The standard text books and widely used statistical packages propose a few popular tests including Bartlett's test, Levene's test and a few adjustments of the latter. Apparently, the popularity of these tests have been based on limited simulation study carried out a few decades ago. The traditional tests, including the classical likelihood ratio test (LRT), are asymptotic in nature, and hence do not perform well for small sample sizes. In this paper we propose a simple parametric bootstrap (PB) modification of the LRT, and compare it against the other popular tests as well as their PB versions in terms of size and power. Our comprehensive simulation study bursts some popularly held myths about the commonly used tests and sheds some new light on this important problem. Though most popular statistical software/packages suggest using Bartlette's test, Levene's test, or modified Levene's test among a few others, our extensive simulation study, carried out under both the normal model as well as several non-normal models clearly shows that a PB version of the modified Levene's test (which does not use the F-distribution cut-off point as its critical value), and Loh's exact test are the “best” performers in terms of overall size as well as power.     

基于BIM的建筑设计思维模式培养——以别墅建筑设计教学实践为例

BIM是未来建筑行业的发展趋势,对于BIM人才的培养迫在眉睫,应用型建筑类本科对于BIM的基础教育至关重要.在低年级本科进行BIM设计思维的培养实践教学,取得了一定的成绩,积累了相关经验,为高年级的复杂建筑BIM设计奠定了基础,也为多专业横向协同做好了准备.     

Cumulative correspondence analysis using orthogonal polynomials

Taguchi's statistic has long been known to be a more appropriate measure of association of the dependence for ordinal variables compared to the Pearson chi-squared statistic. Therefore, there is some advantage in using Taguchi's statistic in the correspondence analysis context when a two-way contingency table consists at least of an ordinal categorical variable. The aim of this paper, considering the contingency table with two ordinal categorical variables, is to show a decomposition of Taguchi's index into linear, quadratic and higher-order components. This decomposition has been developed using Emerson's orthogonal polynomials. Moreover, two case studies to explain the methodology have been analyzed.     

A three-stage variable selection method for supersaturated designs

A supersaturated design (SSD) is a design whose run size is not enough for estimating all main effects. Such a design is commonly used in screening experiments to screen active effects based on the effect sparsity principle. Traditional approaches, such as the ordinary stepwise regression and the best subset variable selection, may not be appropriate in this situation. In this article, a new variable selection method is proposed based on the idea of staged dimensionality reduction. Simulations and several real data studies indicate that the newly proposed method is more effective than the existing data analysis methods.     

Assumption-aware tools and agency; an interrogation of the primary artifacts of the program evaluation and design profession in working with complex evaluands and complex contexts

Like artisans in a professional guild, we evaluators create tools to suit our ever evolving practice. The tools we use as evaluators are the primary artifacts of our profession, reflect our practice and embody an amalgamation of paradigms and assumptions. With the increasing shifts in evaluation purposes from judging program worth to understanding how programs work, the evaluator’s role is changing to that of facilitating stakeholders in a learning process. This involves clarifying purposes and choices, as well as unearthing critical assumptions. In such a role, evaluators become major tool-users and begin to innovate with small refinements or produce completely new tools to fit a specific challenge or context.We interrogate the form and function of 12 tools used by evaluators when working with complex evaluands and complex contexts. The form is described in terms of traditional qualitative techniques and particular characteristics of the elements, use and presentation of each tool. Then the function of each tool is analyzed with respect to articulating assumptions and affecting the agency of evaluators and stakeholders in complex contexts.