Comparison of Operating Characteristics of Commonly Used Sample Size Re-Estimation Procedures in a Two-Stage Design

In group sequential clinical trials, there are several sample size re-estimation methods proposed in the literature that allow for change of sample size at the interim analysis. Most of these methods are based on either the conditional error function or the interim effect size. Our simulation studies compared the operating characteristics of three commonly used sample size re-estimation methods, Chen et al. (2004), Cui et al. (1999), and Muller and Schafer (2001). Gao et al. (2008) extended the CDL method and provided an analytical expression of lower and upper threshold of conditional power where the type I error is preserved. Recently, Mehta and Pocock (2010) extensively discussed that the real benefit of the adaptive approach is to invest the sample size resources in stages and increasing the sample size only if the interim results are in the so called “promising zone” which they define in their article. We incorporated this concept in our simulations while comparing the three methods. To test the robustness of these methods, we explored the impact of incorrect variance assumption on the operating characteristics. We found that the operating characteristics of the three methods are very comparable. In addition, the concept of promising zone, as suggested by MP, gives the desired power and smaller average sample size, and thus increases the efficiency of the trial design.     

Duration of Accrual and Follow-up for Two-Stage Clinical Trials

Group sequential trialswith time to event end points can be complicated to design. Notonly are there unlimited choices for the number of events requiredat each stage, but for each of these choices, there are unlimitedcombinations of accrual and follow-up at each stage that providethe required events. Methods are presented for determining optimalcombinations of accrual and follow-up for two-stage clinicaltrials with time to event end points. Optimization is based onminimizing the expected total study length as a function of theexpected accrual duration or sample size while providing an appropriateoverall size and power. Optimal values of expected accrual durationand minimum expected total study length are given assuming anexponential proportional hazards model comparing two treatmentgroups. The expected total study length can be substantiallydecreased by including a follow-up period during which accrualis suspended. Conditions that warrant an interim follow-up periodare considered, and the gain in efficiency achieved by includingan interim follow-up period is quantified. The gain in efficiencyshould be weighed against the practical difficulties in implementingsuch designs. An example is given to illustrate the use of thesetechniques in designing a clinical trial to compare two chemotherapyregimens for lung cancer. Practical considerations of includingan interim follow-up period are discussed.     

Choice of Baselines in Clinical Trials: A Simulation Study from Statistical Power Perspective

Multiple assessments of an efficacy variable are often conducted prior to the initiation of randomized treatments in clinical trials as baseline information. Two goals are investigated in this article, where the first goal is to investigate the choice of these baselines in the analysis of covariance (ANCOVA) to increase the statistical power, and the second to investigate the magnitude of power loss when a continuous efficacy variable is dichotomized to categorical variable as commonly reported the biomedical literature. A statistical power analysis is developed with extensive simulations based on data from clinical trials in study participants with end stage renal disease (ESRD). It is found that the baseline choices primarily depend on the correlations among the baselines and the efficacy variable, with substantial gains for correlations greater than 0.6 and negligible for less than 0.2. Continuous efficacy variables always give higher statistical power in the ANCOVA modeling and dichotomizing the efficacy variable generally decreases the statistical power by 25%, which is an important practicum in designing clinical trials for study sample size and realistically budget. These findings can be easily applied in and extended to other clinical trials with similar design.     

Development of Adaptive Group Sequential Procedure for Changing Sample Size

In this study, we propose a group sequential procedure that allows the change of necessary sample size at intermediary stage in sequential test. In the procedure, we formulate the conditional power to judge the necessity of the change of sample size in decision rules. Furthermore, we present an integral formula of the power of the test and show how to change the necessary sample size by using the power of the test. In simulation studies, we investigate the characteristics of the change of sample size and the pattern of decision across all stages based on generated normal random numbers.     

A Note on Sample Size Determination for Akaike Information Criterion (AIC) Approach to Clinical Data Analysis

ABSTRACT

Because of its flexibility and usefulness, Akaike Information Criterion (AIC) has been widely used for clinical data analysis. In general, however, AIC is used without paying much attention to sample size. If sample sizes are not large enough, it is possible that the AIC approach does not lead us to the conclusions which we seek. This article focuses on the sample size determination for AIC approach to clinical data analysis. We consider a situation in which outcome variables are dichotomous and propose a method for sample size determination under this situation. The basic idea is also applicable to the situations in which outcome variables have more than two categories or outcome variables are continuous. We present simulation studies and an application to an actual clinical trial.     

Predictive Hierarchic Modeling of Operational Characteristics in Clinical Trials

An analytic methodology for patient enrollment modeling using a Poisson-gamma model is developed by Anisimov & Fedorov (2005–2007). For modeling hierarchic processes associated with enrollment, a new methodology using evolving stochastic processes is proposed. This provides rather general and unified framework to describe various operational processes associated with enrollment. The technique for calculating predictive distributions, mean, and credibility bounds for evolving processes is developed. Some applications to modeling operational characteristics in clinical trials are considered with focus to modeling events associated with incoming and follow-up patients in different settings. For these models, predictive characteristics are derived in a closed form.     

Adaptative Designs for Multi-Arm Clinical Trials: The Play-the-Winner Rule Revisited

Adaptative designs for clinical trials that are based on a generalization of the “play-the-winner” rule are considered as an alternative to previously developed models. Theoretical and numerical results show that these designs perform better for the usual criteria. Bayesian methods are proposed for the statistical analysis of these designs.     

The Dynamic Model for Cancer Relapse Based on Two-Stage Model of Carcinogenesis

In this article, the time from the start of chemotherapy randomization until cancer relapse is of primary interest. Here, cancer relapse refers to the appearance of the first observable malignant clone after therapy. A dynamic model for cancer relapse after chemotherapy is developed. The model differs from the traditional cure rate models in that it takes into consideration the growth kinetics of malignant tumors using a two-stage carcinogenesis model. The survival and hazard functions for cancer relapse time are derived, and a simulation study is performed to validate the underlying model.     

基于尾部指数回归方法的CVaR估计以及实证研究

 在险价值VaR是一种非常重要的金融风险度量方法,近期也有很多关于动态VaR以及条件VaR (CVaR) 等方面的研究。根据金融资产的收益率具有重尾特征这一事实,本文假定金融资产收益率服从重尾分布,并假定重尾分布的尾部指数随着收益率发生变化。本文基于尾部指数回归模型对重尾分布的尾部指数进行估计,进而得到收益率尾部数据所服从的条件分布,并首次运用该方法对条件VaR进行估计。本文对沪深300指数进行了实证研究,得到CVaR的估计,并对估计得到的CVaR的预测效果作出检验,并与传统VaR估计方法进行了对比,实证结果发现本文的方法的预测效果更好。     

Choosing Priors for Constrained Analysis of Variance: Methods Based on Training Data

Abstract. This article combines the best of both objective and subjective Bayesian inference in specifying priors for inequality and equality constrained analysis of variance models. Objectivity can be found in the use of training data to specify a prior distribution, subjectivity can be found in restrictions on the prior to formulate models. The aim of this article is to find the best model in a set of models specified using inequality and equality constraints on the model parameters. For the evaluation of the models an encompassing prior approach is used. The advantage of this approach is that only a prior for the unconstrained encompassing model needs to be specified. The priors for all constrained models can be derived from this encompassing prior. Different choices for this encompassing prior will be considered and evaluated.     

菲茨杰拉德创作能力的变迁——基于词汇测度的统计分析

传统上,不同评论者对同一作家作品的好恶往往会导致对作家创作能力截然不同的评价,这种主观定性评判并没有考虑作品本身的内在特质。计算语言学发现幂律分布在各类文本的词汇测度中具有普遍性,这为定量刻画文学作品的内在特质提供了统计手段。因为作家的创作能力不是一成不变的,不同创作时期的作品反映了作家创作能力的波动,为检验这一论断,以菲茨杰拉德的四部小说为例,对包括类符形符比、词汇增长率和词汇相对熵等在内的词汇测度进行统计分析,发现这三个测度与文中的形符数之间服从幂律关系。故提出一种计算作家创作能力的新测度以及比较其创作能力变化的新方法。菲茨杰拉德作品的实证分析表明：该测度不仅能够有力地印证现有的定性评论观点,而且还能够有效地反映作家创作能力的巅峰与低谷状态。     

政府规模与公共服务支出:来自中国省级水平的经验证据

本文使用我国2000—2006年省级水平的面板数据,应用动态面板数据模型方法对我国政府规模与公共服务支出的关系进行了研究,并用政府消费支出比重和行政人员比重分别衡量政府规模,结果均表明,政府规模对公共服务支出存在显著的正的影响,从而引发了对现在政府机构改革的思考,并据此结论提出了相应的政策与建议。     

财政支出的效率与规模——基于中国的实证分析

运用1978～2004年中国实际国内生产总值和实际政府财政支出的数据，对中国政府规模与经济增长的关系进行实证研究，利用Barro定律及Karras实证方法估计中国政府的政府支出的生产效率及最优规模。并运用Granger因果关系检验了真实GDP、实际政府支出两者之间的因果关系。结果表明：中国的政府财政支出是具有生产性的且政府最优规模为国内生产总值的28．2％（±3％）；中国经济增长是政府规模增长的Granger原因，实证结论支持了瓦格纳定律。     

人民币名义汇率的购买力平价假说检验——基于中美1994-2009年间月度数据的经验分析

基于1994—2009年间中美两国的消费价格指数及人民币对美元名义汇率的月度数据,并结合ADF单位根检验、Engel-Granger两步法检验和Johansen协整检验等方法对购买力平价与人民币名义汇率间的关系进行实证检验。研究结果表明,不仅单变量模型和两变量模型下相对购买力平价对人民币名义汇率的变动不具有解释力,而且三变量模型下国内外消费价格指数对人民币名义汇率的变动也不具有解释力。此外,还对购买力平价在中国失效的原因进行了一定程度的分析。     

基于面板数据模型的生态足迹与经济增长的关系研究——以中部六省为例

生态足迹与经济增长之间的演化关系分析是度量区域经济可持续发展能力的重要方法,因此运用生态足迹模型对中部六省1989—2011年的生态足迹进行了测算,并运用面板数据模型深入分析并比较了中部六省生态足迹与经济增长之间的关系。研究结果表明:中部六省的生态足迹较高,1989—2011年中部六省全部表现为生态赤字,并且生态赤字的数值不断增大;中部六省的生态环境利用效率较低,属于粗放型经济增长方式;中部六省的生态足迹与经济增长的关系存在较大差异,不同省份的生态足迹对经济增长的影响系数差异较大,并且对于不同的生态生产性土地,不同省份的利用效率也有很大不同。     

职业培训对农民工工作转换的影响效应研究——基于辽宁省调查数据的实证分析

利用辽宁省农民工2014年抽样调查数据,对职业培训能否降低农民工的工作转换进行研究。在考虑人力资本异质性的基础上,运用离散选择模型对此做了回答。研究发现,企业提供的职业培训,通过增加专用性人力资本能够显著降低农民工的工作转换;政府或农民工自我提供的培训,由于增加的是通用性人力资本,因而无法有效降低农民工的工作转换。由此,研究认为,要想有效解决农民工的频繁工作变动问题,企业应把农民工纳入到培训体系当中。同时,地方政府可通过补贴政策来协助企业进行农民工培训,帮助企业分担培训成本和风险。     

农民专业合作社规模经济和范围经济研究——基于山西省不同资产规模下的调研数据

采用广义超越对数成本函数模型及确定效应(Fix Period) SUR法,对山西省15个县市44家果蔬农民专业合作社的生产经营进行调研分析.为避免规模差异过大造成的异方差问题,以资产规模200万元为界,将样本合作社分成两组,分别计算其规模经济和范围经济.两组样本合作社总体上具有规模经济和范围经济,但特定产品规模经济(范围经济)各不相同,结论为:第一组样本合作社不能盲目扩大其规模,第二组相对具有竞争优势,各个多样化产出之间的联合生产能节约总成本.     

部门能源消耗的环境外部性测定模型——基于投入产出分析方法

能源消耗责任归属是节能减排标准定量研究中的基础问题。基于生产者和使用者角度的能耗责任归属原则,从投入产出模型内外部两个层面界定能耗强度系数,以此提出能源消耗外部性的测定模型。利用2007年全国投入产出表,对部门能耗强度及外部性测算进行了实证分析。     

“资源诅咒”的成因研究——基于经济结构变化的视角

基于经济结构变化的视角,通过构建一个拥有资源开发部门和制造业部门的两部门经济模型,提出了一个关于＂资源诅咒＂成因的解释框架。研究表明,封闭经济中,＂资源诅咒＂不会产生,而在开放经济中,资源产品在当地较低的加工度、较高的资源禀赋水平、较高的资源价格水平会是资源从＂福音＂走向＂诅咒＂的重要原因。     

产业结构对能源消费的影响及实证分析：基于面板数据模型

国民经济的不同部门具有不同的经济特征,对各种生产要素的需求必然不同,对能源的需求和不同能源品种的需求必然存在差异。为深入了解中国能源消费状况,探讨六大产业部门也即六大能源终端使用部门的能源消费特征,分别建立能源消费总量（EC）、煤炭（EC1）、石油（EC2）、电力（EC3）、天然气（EC4）的面板数据模型。