Item count technique with no floor and ceiling effects

Abstract

Conclusions of sample surveys dealing with matters of privacy are often disputed. Many people either refuse to participate or provide false answers. Indirect questioning techniques are designed so that reliable estimates can be produced while the privacy of the participants is protected. One such method, the Item Count Technique is gaining increasing popularity. However, the original version of the method does not fully protect the privacy of the participants. In this paper we propose a new version of the technique which better protects the privacy of the respondents and does not have the so-called floor and ceiling effects.     

论庭审语言的特征

庭审语言是对案件事实的陈述,具有固定的程序性特征,讲究有根有据,是将事实、证据、法律进行语言加工后形成的一个有机系统。庭审中,法官语言具有威严性;律师语言的总特征是辩论性、针对性;当事人的语言或委屈、或愤怒,富有攻击性。国外庭审语言比较灵动、活泼,国内庭审的语言主要体现为庄重美,两者之间存在较大差异,这主要是由不同的庭审模式造成的。     

言论自由与公正审判的关系研究——以司法对言论的应对为视角

言论自由作为一把双刃剑它在促进司法公正,防止司法腐败的同时也能构成对公正审判的威胁,原因就在于言论自由与公正审判二者背后的价值基础的不同。出于遏制不当的言论对司法公正造成的不良影响的需要,须对公民的言论进行正确的价值引导,同时司法机关自身也要通过相关的制度建设来保证立场的独立与公正,消除民众对司法行为的合理怀疑而引致的猜测性的言论,实现公民言论自由与公正审判的双重目的。     

股权制衡与上市公司派现水平的联动关系研究

基于股权结构内生性的假定,采用面板数据动态地研究了上市公司的派现行为对股权制衡的影响,研究发现公司中小股东并不把上市公司的派现行为(至少是正常派现)视为控股股东的掏空方式,在控股股东完全控制的上市公司这种情况更为显著.考虑到股权结构在静态指标上表现出外生性的一面,同时采用联立方程模型研究发现股权制衡与上市公司派现水平之间存在着联动作用:派现水平越高,上市公司的股权制衡度越低;股权制衡度越低,上市公司的派现水平越高.     

论审判独立的公正与效率价值

应进一步加大审判独立的程度.审判独立是实现审判公正和效率价值的手段.一方面,它对诉讼法程序公正的诸项标准具有保障作用;另一方面,它又是缩减诉讼成本、优化资源配置的重要途径.而当下我国审判不独立是相当严重的,它导致审判公正和效率的双重弱化.     

迅速审判:不同刑事诉讼模式下的理念与制度比较

从刑事诉讼制度的历史发展角度看,迅速审判的理念始终存在。对不同诉讼模式下的迅速审判的理念与制度进行分析,可以根据价值目标的不同将迅速审判的理念与制度划分为纠纷解决型、权力行使型和权利保障型,根据效力范围的不同可划分为局部推进型和整体推进型。迅速审判的类型演进呈现一种进化发展的基本态势,理想的类型搭配是:权利保障型、权力实施型的混合型加整体推进型。可以由此来确定未来一定时期内我国刑事程序改革的基本思路。     

An Asymptotic Analysis of the Logrank Test

Asymptotic expansions for the null distribution of the logrank statistic and its distribution under local proportional hazards alternatives are developed in the case of iid observations. The results, which are derived from the work of Gu (1992) and Taniguchi (1992), are easy to interpret, and provide some theoretical justification for many behavioral characteristics of the logrank test that have been previously observed in simulation studies. We focus primarily upon (i) the inadequacy of the usual normal approximation under treatment group imbalance; and, (ii) the effects of treatment group imbalance on power and sample size calculations. A simple transformation of the logrank statistic is also derived based on results in Konishi (1991) and is found to substantially improve the standard normal approximation to its distribution under the null hypothesis of no survival difference when there is treatment group imbalance. This revised version was published online in July 2006 with corrections to the Cover Date.     

Optimal Bayesian Randomization

Randomization is a puzzle for Bayesians. The intuitive need for randomization is clear, but there is a standard result that Bayesians need not randomize. In this paper we propose a model in which randomization is a strictly optimal procedure. The most important aspect of our model is that there are several parties who make different decisions and observe different data. The result also sheds light on the ethical considerations involving randomization in a clinical trial.     

Methods for exploring treatment effect heterogeneity in subgroup analysis: an application to global clinical trials

Multi‐country randomised clinical trials (MRCTs) are common in the medical literature, and their interpretation has been the subject of extensive recent discussion. In many MRCTs, an evaluation of treatment effect homogeneity across countries or regions is conducted. Subgroup analysis principles require a significant test of interaction in order to claim heterogeneity of treatment effect across subgroups, such as countries in an MRCT. As clinical trials are typically underpowered for tests of interaction, overly optimistic expectations of treatment effect homogeneity can lead researchers, regulators and other stakeholders to over‐interpret apparent differences between subgroups even when heterogeneity tests are insignificant. In this paper, we consider some exploratory analysis tools to address this issue. We present three measures derived using the theory of order statistics, which can be used to understand the magnitude and the nature of the variation in treatment effects that can arise merely as an artefact of chance. These measures are not intended to replace a formal test of interaction but instead provide non‐inferential visual aids, which allow comparison of the observed and expected differences between regions or other subgroups and are a useful supplement to a formal test of interaction. We discuss how our methodology differs from recently published methods addressing the same issue. A case study of our approach is presented using data from the Study of Platelet Inhibition and Patient Outcomes (PLATO), which was a large cardiovascular MRCT that has been the subject of controversy in the literature. An R package is available that implements the proposed methods. Copyright © 2014 John Wiley & Sons, Ltd.     

Phase I trial design for drug combinations with Bayesian model averaging

Various statistical models have been proposed for two‐dimensional dose finding in drug‐combination trials. However, it is often a dilemma to decide which model to use when conducting a particular drug‐combination trial. We make a comprehensive comparison of four dose‐finding methods, and for fairness, we apply the same dose‐finding algorithm under the four model structures. Through extensive simulation studies, we compare the operating characteristics of these methods in various practical scenarios. The results show that different models may lead to different design properties and that no single model performs uniformly better in all scenarios. As a result, we propose using Bayesian model averaging to overcome the arbitrariness of the model specification and enhance the robustness of the design. We assign a discrete probability mass to each model as the prior model probability and then estimate the toxicity probabilities of combined doses in the Bayesian model averaging framework. During the trial, we adaptively allocated each new cohort of patients to the most appropriate dose combination by comparing the posterior estimates of the toxicity probabilities with the prespecified toxicity target. The simulation results demonstrate that the Bayesian model averaging approach is robust under various scenarios. Copyright © 2015 John Wiley & Sons, Ltd.