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。     

具有高投标成本的多属性逆向拍卖博弈模型

越来越多像GE这样的大型企业在利用多属性逆向拍卖选择新的供应商采购产品时,通常会设定较高的固定投标成本。针对此情况,建立了三阶段的非合作博弈模型,并利用求解子博弈纳什均衡策略的方法,推导出了供应商的最优投标价格。并得到以下两个主要结论:一是供应商的最优投标策略是按照生产产品真实的质量和交货期进行投标,且投标价格为最优投标价格;二是采购商的最优策略是选择投标价格最高的供应商作为拍卖获胜者,这一违反直觉的结论。这是合理的,因为投标价格最高的供应商也是类型最优的供应商,也即投标质量和投标交货期组合最优的供应商。最后,利用数值实验验证了模型的有效性,并显示出设定相对较高的固定投标成本对采购商来说是有利的。     

委托代理框架下项目投资的最优合同设计

在不确定的市场环境下,企业的投资机会具有期权特点。本文在委托代理框架下,研究实物期权投资中的最优合同设计问题。考虑一个委托代理制的企业,委托人拥有推迟项目投资的期权,授予代理人执行该投资期权。信息不对称下,代理人有隐藏信息转移现金流的动机。为了实现自身利益的最大化,委托人设计合同,在该合同下,代理人将揭示真实的信息。在信息对称与信息不对称的情形下,分别建立实物期权模型,得到了委托人设计的最优合同。并通过数值分析,得到了如下的主要结论,信息不对称下,与信息对称相比,高成本的项目投资时机推迟,低成本的项目时机提前,投资期权价值减少。委托人的期权价值随着审核效率的提高而增大。当代理人越没有耐心时,委托人的期权价值越大。本文的研究为现实中的委托代理框架下的实物期权投资决策提供了一定的指导意义。     

中国风电固定上网电价政策的实物期权理论与实证分析

本文使用实物期权方法研究中国目前实施的风电固定上网电价政策。首先说明风电项目投资的实物期权原理,并建立风电固定上网电价政策实物期权模型。然后,从理论上证明了风电最优投资电价与上网电价波动率成正比(固定上网电价理论依据,即政府为了最大限度激励投资者,使得上网电价波动率等于0);最优投资电价与上网电价期望增长率成正比(固定上网电价调整理论依据)。最后,选取河北张北油篓沟元山子风电场工程项目进行实证分析,通过上网电价期望增长率调整的数值分析得到中国风电上网电价平均每年降低幅度应该为1%左右。本文为我国可再生能源电价调整提供参考。     

Non Asymptotic Minimax Estimation of Functionals with Noisy Observations

The minimax estimation of functionals by a finite number of noisy observations is considered. A new way to formalize the problem that enables one to calculate non asymptotic optimal estimates is proposed. The calculations can be turned into and executed as a computer algorithm or carried out analytically under week assumptions on random variables. Some examples are considered.     

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.