A case study in identifying targeted patients population in major depressive disorder by enhanced enrichment design

Despite advances in clinical trial design, failure rates near 80% in phase 2 and 50% in phase 3 have recently been reported. The challenges to successful drug development are particularly acute in central nervous system trials such as for pain, schizophrenia, mania, and depression because high‐placebo response rates lessen assay sensitivity, diminish estimated treatment effect sizes, and thereby decrease statistical power. This paper addresses the importance of rigorous patient selection in major depressive disorder trials through an enhanced enrichment paradigm. This approach led to a redefinition of an ongoing, blinded phase 3 trial algorithm for patient inclusion (1) to eliminate further randomization of transient placebo responders and (2) to exclude previously randomized transient responders from the primary analysis of the double blind phase of the trial. It is illustrated for a case study for the comparison between brexpiprazole + antidepressant therapy and placebo + antidepressant therapy. Analysis of the primary endpoint showed that efficacy of brexpiprazole versus placebo could not be established statistically if the original algorithm for identification of placebo responders was used, but the enhanced enrichment approach did statistically demonstrate efficacy. Additionally, the enhanced enrichment approach identified a target population with a clinically meaningful treatment effect. Through its successful identification of a target population, the innovative enhanced enrichment approach enabled the demonstration of a positive treatment effect in a very challenging area of depression research.     

团体心理辅导提升留守经历大学生心理资本研究——以西南石油大学为例

为了了解团体心理辅导对留守经历大学生心理资本水平的干预和提升效果,以心理资本问卷作为评估工具,采用实验组对照组前后测的实验设计,在西南石油大学公开招募留守经历大学生作为被试。研究发现：实验组前后测的心理资本得分有显著的差异,表现为实验组的心理资本及各维度的后测得分显著高于前测得分,对照组的心理资本前后测得分无显著差异;实验组与对照组的心理资本后测得分差异显著,表现为实验组的心理资本及各维度的后测得分显著高于前测得分;实验组中,绝大多数学生对整个团体活动感到满意,认为在活动中有所收获,觉得团体辅导对重新看待自己的过去和成长经历有较大帮助。     

允许直达的混合轴辐式快递网络规划模型与算法研究

在快递企业的日常运营中,当两个城市之间的快递业务量达到一定规模之后会允许该城市对之间开展直达递送。但是这一规则通常仅在快递网络规划完成之后由相关子公司提请实施。为弥补这一实际规则的局部最优性缺陷,本论文将直达问题纳入网络规划决策,基于全局优化的视角构建了允许直达的混合轴幅式快递网络规划决策模型,设计了相应的求解流程,并对其中的指派关系决策构建了遗传算法。基于国内标杆企业的数值案例计算证明了该决策模型与求解方法的有效性,研究结果还说明,相对于纯轴辐式结构,允许直达的混合轴幅式网络结构有助于降低网络总成本,同时在直达线路上能够有效的降低迂回、提高服务时效性和服务水平。     

教育改善贫困效应的地区异质性研究

教育对于增加收入、改善贫困无疑是一个重要的途径。在目前多维贫困越来越受到重视的情况下,本文在收入贫困和多维贫困两种贫困识别标准下,分析了教育改善贫困的效应在地区之间的异质性,考察了该效应是否受地区经济发展的影响。根据分层LOGIT模型的估计,本文认为地区经济发展水平是发挥教育改善收入贫困效应的重要舞台,对于多维贫困的改善效应则不明显。本文得到的一个重要扶贫政策建议是要发挥教育改善贫困的效应,必须注重精准扶贫与发展地区经济相结合;但仅仅发展经济,不一定能保证教育对多维贫困的改善效应,减轻多维贫困需要更加重视公共产品供给和民生保证。     

Estimation of the Mean of a Sensitive Variable in the Presence of Auxiliary Information

Sousa et al. (2010 Sousa , R. , Shabbir , J. , Real , P. C. , Gupta , S. ( 2010 ). Ratio estimation of the mean of a sensitive variable in the presence of auxiliary information . J. Statist. Theor. Prac. 4 ( 3 ): 495 – 507 .[Taylor & Francis Online] , [Google Scholar]) introduced a ratio estimator for the mean of a sensitive variable and showed that this estimator performs better than the ordinary mean estimator based on a randomized response technique (RRT). In this article, we introduce a regression estimator that performs better than the ratio estimator even for modest correlation between the primary and the auxiliary variables. The underlying assumption is that the primary variable is sensitive in nature but a non sensitive auxiliary variable exists that is positively correlated with the primary variable. Expressions for the Bias and MSE (Mean Square Error) are derived based on the first order of approximation. It is shown that the proposed regression estimator performs better than the ratio estimator and the ordinary RRT mean estimator (that does not utilize the auxiliary information). We also consider a generalized regression-cum-ratio estimator that has even smaller MSE. An extensive simulation study is presented to evaluate the performances of the proposed estimators in relation to other estimators in the study. The procedure is also applied to some financial data: purchase orders (a sensitive variable) and gross turnover (a non sensitive variable) in 2009 for a population of 5,336 companies in Portugal from a survey on Information and Communication Technologies (ICT) usage.     

A Hybrid Geometric Phase II/III Clinical Trial Design based on Treatment Failure Time and Toxicity

The problem of comparing several experimental treatments to a standard arises frequently in medical research. Various multi-stage randomized phase II/III designs have been proposed that select one or more promising experimental treatments and compare them to the standard while controlling overall Type I and Type II error rates. This paper addresses phase II/III settings where the joint goals are to increase the average time to treatment failure and control the probability of toxicity while accounting for patient heterogeneity. We are motivated by the desire to construct a feasible design for a trial of four chemotherapy combinations for treating a family of rare pediatric brain tumors. We present a hybrid two-stage design based on two-dimensional treatment effect parameters. A targeted parameter set is constructed from elicited parameter pairs considered to be equally desirable. Bayesian regression models for failure time and the probability of toxicity as functions of treatment and prognostic covariates are used to define two-dimensional covariate-adjusted treatment effect parameter sets. Decisions at each stage of the trial are based on the ratio of posterior probabilities of the alternative and null covariate-adjusted parameter sets. Design parameters are chosen to minimize expected sample size subject to frequentist error constraints. The design is illustrated by application to the brain tumor trial.     

Efficient circular neighbour designs for spatial interference model

We consider circular block designs for field-trials when there are two-sided spatial interference between neighbouring plots of the same blocks. The parameter of interest is total effects that is the sum of direct effect of treatment and neighbour effects, which correspond to the use of a single treatment in the whole field. We determine universally optimal approximate designs. When the number of blocks may be large, we propose efficient exact designs generated by a single sequence of treatment. We also give efficiency factors of the usual binary block neighbour balanced designs which can be used when the number of blocks is small.     

Optimal design and maintenance of a repairable multi-state system with standby components

The configuration of a repairable system directly influences its performance measures, such as mean time between failures and steady state availability. Additionally, maintenance strategies such as corrective, preventive, and condition-based can affect the performance of the system. The objective of this work is to investigate the trade-offs between the configuration of a repairable multi-state system with binary components and its maintenance strategy. The corresponding stochastic process for the proposed model is formulated using the continuous-time Markov process and important performance measures of such a multi-state system are derived. An optimization model is introduced for the cost-effective design of this repairable multi-state system. The results are demonstrated using a numerical example for a power generation system.     

Addition of runs to an s-level supersaturated design

The purpose of the present work is to extend the work of Gupta et al. (2010) to s  -level column balanced supersaturated designs. Addition of runs to an existing E(χ²)-optimal$E({\chi }^2)\mathrm{-}\mathrm{optimal}$ supersaturated design and to study the optimality of the resulting design is an important issue. This paper considers the study of the optimality of the resulting design. A lower bound to E(χ²)$E({\chi }^2)$ has been obtained for the extended supersaturated designs. Some examples and a small catalogue of E(χ²)-optimal$E({\chi }^2)\mathrm{-}\mathrm{optimal}$ supersaturated designs are also presented.     

On the correct regression function (in L2) and its applications when the dimension of the covariate vector is random

We derive the optimal regression function (i.e., the best approximation in the L₂ sense) when the vector of covariates has a random dimension. Furthermore, we consider applications of these results to problems in statistical regression and classification with missing covariates. It will be seen, perhaps surprisingly, that the correct regression function for the case with missing covariates can sometimes perform better than the usual regression function corresponding to the case with no missing covariates. This is because even if some of the covariates are missing, an indicator random variable δ$\delta$, which is always observable, and is equal to 1 if there are no missing values (and 0 otherwise), may have far more information and predictive power about the response variable Y than the missing covariates do. We also propose kernel-based procedures for estimating the correct regression function nonparametrically. As an alternative estimation procedure, we also consider the least-squares method.