Data‐driven treatment selection for seamless phase II/III trials incorporating early‐outcome data

Seamless phase II/III clinical trials are conducted in two stages with treatment selection at the first stage. In the first stage, patients are randomized to a control or one of k > 1 experimental treatments. At the end of this stage, interim data are analysed, and a decision is made concerning which experimental treatment should continue to the second stage. If the primary endpoint is observable only after some period of follow‐up, at the interim analysis data may be available on some early outcome on a larger number of patients than those for whom the primary endpoint is available. These early endpoint data can thus be used for treatment selection. For two previously proposed approaches, the power has been shown to be greater for one or other method depending on the true treatment effects and correlations. We propose a new approach that builds on the previously proposed approaches and uses data available at the interim analysis to estimate these parameters and then, on the basis of these estimates, chooses the treatment selection method with the highest probability of correctly selecting the most effective treatment. This method is shown to perform well compared with the two previously described methods for a wide range of true parameter values. In most cases, the performance of the new method is either similar to or, in some cases, better than either of the two previously proposed methods. © 2014 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.     

A Bayesian adaptive blinded sample size adjustment method for risk differences

Adaptive sample size adjustment (SSA) for clinical trials consists of examining early subsets of on trial data to adjust estimates of sample size requirements. Blinded SSA is often preferred over unblinded SSA because it obviates many logistical complications of the latter and generally introduces less bias. On the other hand, current blinded SSA methods for binary data offer little to no new information about the treatment effect, ignore uncertainties associated with the population treatment proportions, and/or depend on enhanced randomization schemes that risk partial unblinding. I propose an innovative blinded SSA method for use when the primary analysis is a non‐inferiority or superiority test regarding a risk difference. The method incorporates evidence about the treatment effect via the likelihood function of a mixture distribution. I compare the new method with an established one and with the fixed sample size study design, in terms of maximization of an expected utility function. The new method maximizes the expected utility better than do the comparators, under a range of assumptions. I illustrate the use of the proposed method with an example that incorporates a Bayesian hierarchical model. Lastly, I suggest topics for future study regarding the proposed methods. Copyright © 2015 John Wiley & Sons, Ltd.     

A Comparison Of Two Adaptive Sampling Designs

Stratified sampling is a technique commonly used for ecological surveys. In this study there appears to be little gain in using a stratified design with adaptive cluster sampling. Two-phase adaptive sampling is preferable to adaptive cluster sampling. Even though two-phase adaptive sampling can give biased estimates, it is found that two-phase adaptive sampling has a lower MSE than adaptive cluster sampling for most populations.     

A Bayesian model for local smoothing in kernel density estimation

A new procedure is proposed for deriving variable bandwidths in univariate kernel density estimation, based upon likelihood cross-validation and an analysis of a Bayesian graphical model. The procedure admits bandwidth selection which is flexible in terms of the amount of smoothing required. In addition, the basic model can be extended to incorporate local smoothing of the density estimate. The method is shown to perform well in both theoretical and practical situations, and we compare our method with those of Abramson (The Annals of Statistics 10: 1217–1223) and Sain and Scott (Journal of the American Statistical Association 91: 1525–1534). In particular, we note that in certain cases, the Sain and Scott method performs poorly even with relatively large sample sizes.We compare various bandwidth selection methods using standard mean integrated square error criteria to assess the quality of the density estimates. We study situations where the underlying density is assumed both known and unknown, and note that in practice, our method performs well when sample sizes are small. In addition, we also apply the methods to real data, and again we believe our methods perform at least as well as existing methods.     

适配均衡与多元协同：社区生活垃圾分类的政策工具选择

选取31个省级地方政府层面的73份社区生活垃圾分类政策文本,从政策工具的视角,采用扎根理论分析方法,研究社区生活垃圾分类政策系统的时空结构、政策工具与政策目标的适配以及政策工具之间的协同状况。结果发现,社区生活垃圾分类政策工具、政策目标和政策主体行动之间存在着适配性、协同性问题：在一级政策工具的选择与使用上以权威性工具为主,其次是社会性工具,再次是经济性工具;在次级政策工具的选择与使用方面,各有侧重,政策系统内部结构呈现出不均衡态势;地区间在政策工具使用的时间和空间序列上存在不协同、变化差异较大、波动性显著的特性。因此,优化社区生活垃圾分类政策工具的选择,需要充分发挥政策工具的整体性功能,保持政策工具的内部结构平衡,拓展与创新地方政策工具内涵。     

再生核Hilbert空间展开的函数型回归模型变量选择

针对协变量是函数型、响应变量是标量的多元函数型回归模型,文章提出了函数系数基于再生核Hilbert空间展开的变量选择方法。首先,利用带积分余项的泰勒展开式和再生核Hilbert空间内积性质将模型转化为结构化形式,其次,通过自适应弹性网惩罚对结构化模型中的组间和组内系数同时进行压缩。结果证明了这种压缩估计具有Oracle性质,蒙特卡罗模拟结果也显示新方法在不同样本量、不同噪声和变量相关性干扰下均优于基于普通基函数展开的变量选择方法,且尤其适用于原始协变量高度相关的情形。最后,通过分析一个商品房平均销售价格影响因素数据演示了新方法的应用。     

一类新的自适应非单调谱投影梯度法

给出了求解凸约束优化的一类新的自适应非单调谱投影梯度法.通过引入具有自适应性的权重参数,使算法在迭代过程中能自动调节非单调策略. 在适当条件下证明了算法的收敛性.数值试验结果表明,该算法在一定程度上能减少在线搜索过程中对非单调参数M的依赖.     

Daisee: Adaptive importance sampling by balancing exploration and exploitation

We study adaptive importance sampling (AIS) as an online learning problem and argue for the importance of the trade-off between exploration and exploitation in this adaptation. Borrowing ideas from the online learning literature, we propose Daisee, a partition-based AIS algorithm. We further introduce a notion of regret for AIS and show that Daisee has $𝒪(\sqrt{T}{(\log T)}^{\frac{3}{4}})$ cumulative pseudo-regret, where $T$ is the number of iterations. We then extend Daisee to adaptively learn a hierarchical partitioning of the sample space for more efficient sampling and confirm the performance of both algorithms empirically.     

基于温尼科特“客体关系”理论的幼儿入园适应行为诠释

幼儿入园是幼儿面临的一次发展性危机。幼儿入园适应行为不尽相同,从温尼科特客体关系理论视角分析幼儿入园行为,可将幼儿分为顺从型、狂暴型、依恋型、散漫型和无助型。在此基础上提出耐心分析幼儿实际心理发展水平、提供抱持环境促进幼儿适应、创设环境满足幼儿利用过渡空间的需要等建议。     

目标取向和过时感知能力对两类销售行为的影响

销售努力和适应性销售是两类最基本的销售行为,探寻这两种销售行为的决定因素是销售管理领域的热点研究问题.基于教育心理学和销售力管理的相关研究成果,探讨3种目标取向与两类消费行为的关系,并通过引入过时这一概念,探究不同目标取向导致不同程度的销售努力和适应性销售的内在机理.通过对9家公司160名销售人员的问卷调查,运用结构方程模型,考察销售人员的不同目标取向与销售行为和销售力过时感知能力以及销售力过时感知能力与销售行为的关系,并重点检验销售力过时感知能力对目标取向与销售行为关系的中介效应.实证研究结果表明,目标取向对销售力过时感知能力和销售行为均有显著影响,销售力过时感知能力也会显著影响销售行为,销售力过时感知能力在目标取向与销售行为的关系中起到一定的中介作用.