Heteroscedasticity and Distributional Assumptions in the Censored Regression Model

Data censoring causes ordinary least-square estimators of linear models to be biased and inconsistent. The Tobit estimator yields consistent estimators in the presence of data censoring if the errors are normally distributed. However, nonnormality or heteroscedasticity results in the Tobit estimators being inconsistent. Various estimators have been proposed for circumventing the normality assumption. Some of these estimators include censored least absolute deviations (CLAD), symmetrically censored least-square (SCLS), and partially adaptive estimators. CLAD and SCLS will be consistent in the presence of heteroscedasticity; however, SCLS performs poorly in the presence of asymmetric errors. This article extends the partially adaptive estimation approach to accommodate possible heteroscedasticity as well as nonnormality. A simulation study is used to investigate the estimators’ relative performance in these settings. The partially adaptive censored regression estimators have little efficiency loss for censored normal errors and appear to outperform the Tobit and semiparametric estimators for nonnormal error distributions and be less sensitive to the presence of heteroscedasticity. An empirical example is considered, which supports these results.     

Response-adaptive trial designs with accelerated Thompson sampling

In a clinical trial, sometimes it is desirable to allocate as many patients as possible to the best treatment, in particular, when a trial for a rare disease may contain a considerable portion of the whole target population. The Gittins index rule is a powerful tool for sequentially allocating patients to the best treatment based on the responses of patients already treated. However, its application in clinical trials is limited due to technical complexity and lack of randomness. Thompson sampling is an appealing approach, since it makes a compromise between optimal treatment allocation and randomness with some desirable optimal properties in the machine learning context. However, in clinical trial settings, multiple simulation studies have shown disappointing results with Thompson samplers. We consider how to improve short-run performance of Thompson sampling and propose a novel acceleration approach. This approach can also be applied to situations when patients can only be allocated by batch and is very easy to implement without using complex algorithms. A simulation study showed that this approach could improve the performance of Thompson sampling in terms of average total response rate. An application to a redesign of a preference trial to maximize patient's satisfaction is also presented.     

A stochastically curtailed two-arm randomised phase II trial design for binary outcomes

Randomised controlled trials are considered the gold standard in trial design. However, phase II oncology trials with a binary outcome are often single-arm. Although a number of reasons exist for choosing a single-arm trial, the primary reason is that single-arm designs require fewer participants than their randomised equivalents. Therefore, the development of novel methodology that makes randomised designs more efficient is of value to the trials community. This article introduces a randomised two-arm binary outcome trial design that includes stochastic curtailment (SC), allowing for the possibility of stopping a trial before the final conclusions are known with certainty. In addition to SC, the proposed design involves the use of a randomised block design, which allows investigators to control the number of interim analyses. This approach is compared with existing designs that also use early stopping, through the use of a loss function comprised of a weighted sum of design characteristics. Comparisons are also made using an example from a real trial. The comparisons show that for many possible loss functions, the proposed design is superior to existing designs. Further, the proposed design may be more practical, by allowing a flexible number of interim analyses. One existing design produces superior design realisations when the anticipated response rate is low. However, when using this design, the probability of rejecting the null hypothesis is sensitive to misspecification of the null response rate. Therefore, when considering randomised designs in phase II, we recommend the proposed approach be preferred over other sequential designs.     

A Bayesian adaptive design for biosimilar trials with time-to-event endpoint

A biosimilar drug is a biological product that is highly similar to and at the same time has no clinically meaningful difference from licensed product in terms of safety, purity, and potency. Biosimilar study design is essential to demonstrate the equivalence between biosimilar drug and reference product. However, existing designs and assessment methods are primarily based on binary and continuous endpoints. We propose a Bayesian adaptive design for biosimilarity trials with time-to-event endpoint. The features of the proposed design are twofold. First, we employ the calibrated power prior to precisely borrow relevant information from historical data for the reference drug. Second, we propose a two-stage procedure using the Bayesian biosimilarity index (BBI) to allow early stop and improve the efficiency. Extensive simulations are conducted to demonstrate the operating characteristics of the proposed method in contrast with some naive method. Sensitivity analysis and extension with respect to the assumptions are presented.     

Bayesian MARS

A Bayesian approach to multivariate adaptive regression spline (MARS) fitting (Friedman, 1991) is proposed. This takes the form of a probability distribution over the space of possible MARS models which is explored using reversible jump Markov chain Monte Carlo methods (Green, 1995). The generated sample of MARS models produced is shown to have good predictive power when averaged and allows easy interpretation of the relative importance of predictors to the overall fit.     

城市废弃物资源化共生网络脆弱性影响机制——基于SCP与CAS融合的分析视角

在内外因素的交互干扰下,城市废弃物资源化共生网络表现得格外脆弱,探寻其脆弱性的理论内涵、影响因素及其作用机制是开展脆弱性治理的前提。借鉴复杂系统脆弱性理论,从能力和绩效两个角度解析其理论内涵,指出系统脆弱性在能力上可以用敏感性、适应性和恢复力予以刻画,在绩效上表现为增值性不强、共享性不足和生态性不佳。通过梳理国内外研究文献,从共生单元、网络结构、共生机制、网络氛围和环境不确定性5个维度提炼出脆弱性影响因素;以网络动态能力为结合点,将SCP（Structure Conduct Performance）和CAS（Complexity Adaptive System）两种理论有机融合,构建一个以共生单元素质、网络结构特征、共生机制属性、政策有效性为外因变量,以网络动态能力为中介变量,以网络脆弱性为结果变量,以网络氛围和环境不确定性为调节变量的理论框架。未来可利用大样本调查方法对该理论框架进行实证检验,探讨城市废弃物资源化共生网络脆弱性的形成机理。     

转型经济体制下创业团队的先前经验、无力感与适应能力

在资源依赖理论的基础上,从创业团队的先前经验出发,通过分析4家高科技创业企业的案例资料,考察了创业团队无力感与适应能力之间的关联性,并分析了创业团队的认知趋同性对该作用过程的影响机制,从而为新创企业提高在动态环境中的生存能力提供建议.研究表明,创业团队先前经验可以降低团队成员的无力感,从而提升团队的适应能力,且当创业团队认知趋同性越高时,无力感与适应能力的负向作用越强.     

数字金融对农户气候适应性行为的影响

气候适应性技术采用率低下已成为制约农业可持续发展的重要因素,数字金融可能影响农户的气候适应性行为。基于河南、陕西、山西三省1 384份农户微观调查数据,运用内生转换回归模型构建反事实分析框架,实证分析数字金融使用对农户气候适应性行为的影响效应及其作用机制。研究发现:使用数字金融能显著促进农户气候适应性技术采纳行为,具体表现为在反事实假设下,使用数字金融的农户若未使用其气候适应性技术采纳程度将下降;未使用数字金融的农户如果使用了,其气候适应性技术采纳程度将上升。机制分析表明,数字金融能够提高借贷易得性与信息易得性,进而促进农户采纳气候适应性行为,农户对于金融包容性的认知能够正向增强数字金融对农户气候适应性行为的影响效应。异质性分析表明,数字金融使用对于资本型适应性行为影响的边际效应最大,在反事实假设下也表现为数字金融使用对农户资本型适应性行为提升效果最强;数字金融对农业收入占家庭收入比率较高、接受过培训的农户采纳气候适应性技术促进效应更高。     

Adaptive designs for IVPT data with mixed scaled average bioequivalence

In vitro permeation tests (IVPT) offer accurate and cost-effective development pathways for locally acting drugs, such as topical dermatological products. For assessment of bioequivalence, the FDA draft guidance on generic acyclovir 5% cream introduces a new experimental design, namely the single-dose, multiple-replicate per treatment group design, as IVPT pivotal study design. We examine the statistical properties of its hypothesis testing method—namely the mixed scaled average bioequivalence (MSABE). Meanwhile, some adaptive design features in clinical trials can help researchers make a decision earlier with fewer subjects or boost power, saving resources, while controlling the impact on family-wise error rate. Therefore, we incorporate MSABE in an adaptive design combining the group sequential design and sample size re-estimation. Simulation studies are conducted to study the passing rates of the proposed methods—both within and outside the average bioequivalence limits. We further consider modifications to the adaptive designs applied for IVPT BE trials, such as Bonferroni's adjustment and conditional power function. Finally, a case study with real data demonstrates the advantages of such adaptive methods.     

基于自适应迭代更新的函数型数据聚类方法研究

函数型数据的稀疏性和无穷维特性使得传统聚类分析失效。针对此问题,本文在界定函数型数据概念与内涵的基础上提出了一种自适应迭代更新聚类分析。首先,基于数据参数信息实现无穷维函数空间向有限维多元空间的过渡;在此基础上,依据变量信息含量的差异构建了自适应赋权聚类统计量,并依此为函数型数据的相似性测度进行初始类别划分;进一步地,在给定阈值限制下,对所有函数的初始类别归属进行自适应迭代更新,将收敛的优化结果作为最终的类别划分。随机模拟和实证检验表明,与现有的同类函数型聚类分析相比,文中方法的分类正确率显著提高,体现了新方法的相对优良性和实际问题应用中的有效性。