Development of predictive signatures for treatment selection in precision medicine with survival outcomes

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker‐positive and biomarker‐negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood‐based change‐point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change‐point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially.     

Response‐adaptive designs for binary responses: How to offer patient benefit while being robust to time trends?

Response‐adaptive randomisation (RAR) can considerably improve the chances of a successful treatment outcome for patients in a clinical trial by skewing the allocation probability towards better performing treatments as data accumulates. There is considerable interest in using RAR designs in drug development for rare diseases, where traditional designs are not either feasible or ethically questionable. In this paper, we discuss and address a major criticism levelled at RAR: namely, type I error inflation due to an unknown time trend over the course of the trial. The most common cause of this phenomenon is changes in the characteristics of recruited patients—referred to as patient drift. This is a realistic concern for clinical trials in rare diseases due to their lengthly accrual rate. We compute the type I error inflation as a function of the time trend magnitude to determine in which contexts the problem is most exacerbated. We then assess the ability of different correction methods to preserve type I error in these contexts and their performance in terms of other operating characteristics, including patient benefit and power. We make recommendations as to which correction methods are most suitable in the rare disease context for several RAR rules, differentiating between the 2‐armed and the multi‐armed case. We further propose a RAR design for multi‐armed clinical trials, which is computationally efficient and robust to several time trends considered.     

Double shrinkage estimators for large sparse covariance matrices

Covariance matrices play an important role in many multivariate techniques and hence a good covariance estimation is crucial in this kind of analysis. In many applications a sparse covariance matrix is expected due to the nature of the data or for simple interpretation. Hard thresholding, soft thresholding, and generalized thresholding were therefore developed to this end. However, these estimators do not always yield well-conditioned covariance estimates. To have sparse and well-conditioned estimates, we propose doubly shrinkage estimators: shrinking small covariances towards zero and then shrinking covariance matrix towards a diagonal matrix. Additionally, a richness index is defined to evaluate how rich a covariance matrix is. According to our simulations, the richness index serves as a good indicator to choose relevant covariance estimator.     

Adaptive designs for single‐arm phase II trials in oncology

Clinical phase II trials in oncology are conducted to determine whether the activity of a new anticancer treatment is promising enough to merit further investigation. Two‐stage designs are commonly used for this situation to allow for early termination. Designs proposed in the literature so far have the common drawback that the sample sizes for the two stages have to be specified in the protocol and have to be adhered to strictly during the course of the trial. As a consequence, designs that allow a higher extent of flexibility are desirable. In this article, we propose a new adaptive method that allows an arbitrary modification of the sample size of the second stage using the results of the interim analysis or external information while controlling the type I error rate. If the sample size is not changed during the trial, the proposed design shows very similar characteristics to the optimal two‐stage design proposed by Chang et al. (Biometrics 1987; 43:865–874). However, the new design allows the use of mid‐course information for the planning of the second stage, thus meeting practical requirements when performing clinical phase II trials in oncology. Copyright © 2012 John Wiley & Sons, Ltd.     

Risk Perception and Information Processing: The Development and Validation of a Questionnaire to Assess Self‐Reported Information Processing

The role of information processing in understanding people's responses to risk information has recently received substantial attention. One limitation of this research concerns the unavailability of a validated questionnaire of information processing. This article presents two studies in which we describe the development and validation of the Information‐Processing Questionnaire to meet that need. Study 1 describes the development and initial validation of the questionnaire. Participants were randomized to either a systematic processing or a heuristic processing condition after which they completed a manipulation check and the initial 15‐item questionnaire and again two weeks later. The questionnaire was subjected to factor reliability and validity analyses on both measurement times for purposes of cross‐validation of the results. A two‐factor solution was observed representing a systematic processing and a heuristic processing subscale. The resulting scale showed good reliability and validity, with the systematic condition scoring significantly higher on the systematic subscale and the heuristic processing condition significantly higher on the heuristic subscale. Study 2 sought to further validate the questionnaire in a field study. Results of the second study corresponded with those of Study 1 and provided further evidence of the validity of the Information‐Processing Questionnaire. The availability of this information‐processing scale will be a valuable asset for future research and may provide researchers with new research opportunities.     

The effect of cognitive diversity on the illusion of control bias in strategic decisions: An experimental investigation

By integrating cognitive diversity into debiasing literature, this paper contributes towards opening the black box of executive judgment. Based on information processing theory we investigate the role of cognitive diversity in strategic decision making. We apply a vignette-based experimental research design to examine the effect of cognitive diversity in teams on decision maker's illusion of control. The results of these experiments provide evidence for a positive influence of high cognitive diversity for debiasing judgment while similarly indicating no such effect for groups with low cognitive diversity. These findings suggest that group composition aspects can play an important role for improving judgment in decision making teams and open promising new avenues for studying debiasing in behavioral strategy research.     

Spectral Estimation of Covolatility from Noisy Observations Using Local Weights

We propose localized spectral estimators for the quadratic covariation and the spot covolatility of diffusion processes, which are observed discretely with additive observation noise. The appropriate estimation for time‐varying volatilities is based on an asymptotic equivalence of the underlying statistical model to a white‐noise model with correlation and volatility processes being constant over small time intervals. The asymptotic equivalence of the continuous‐time and discrete‐time experiments is proved by a construction with linear interpolation in one direction and local means for the other. The new estimator outperforms earlier non‐parametric methods in the literature for the considered model. We investigate its finite sample size characteristics in simulations and draw a comparison between various proposed methods.     

不同性质汉语隐喻句认知加工的fMRI研究

有关隐喻认知研究的一个核心问题是:隐喻加工机制与一般言语理解相比是否具有独特性以及隐喻理解伴随的神经生理机制是否有其独特的模式? 对此问题的回答需要对隐喻性质进行区分。常见的熟悉隐喻可进一步区分为词源性隐喻与引发性隐喻,其中词源性隐喻的隐喻义属于词语多个词义中的一个延伸义,而引发性隐喻则是将两个分属于不同概念领域的概念进行非字面的语义联结。词源性隐喻句和引发性隐喻句的理解包含了与一般语义句理解不同的加工内容。相比一般句,词源性隐喻句特异性地激活了与语义竞争相关的左侧颞中回、颞上回以及与加工深度有关的额下回,而引发性隐喻句则在左侧额下回有更多激活。隐喻的内在加工过程和神经机制并不单一,探讨隐喻理解与一般语义理解的异同需要考虑具体的隐喻性质。     

证券分析师性别、盈余预测偏差与纠偏能力

证券分析师个人特征是其能力的重要表征.文章选取2007-2014年我国资本市场相关数据,研究了证券分析师的性别对盈余预测偏差的影响,以及盈余预测偏差的时间序列特征,实证结果显示:第一,整体上男性分析师的盈余预测偏差大于女性分析师;第二,男性分析师对于盈余预测偏差的纠正能力高于女性分析师;第三,随着对公司跟踪时间的增加,男性分析师的盈余预测偏差下降速度更快.研究结果说明,对于分析师跟踪时间较短的公司,女性分析师的盈余预测偏差较小,而对于受分析师长期跟踪的公司,男性分析师的盈余预测结果更准确.研究结论对投资者依据分析师性别判定其盈利预测准确性具有重要意义.     

落实最严格水资源管理的适应性政策选择研究

由于最严格水资源管理存在着水资源供给、用水总量、用水关系和用水管理等诸多不确定,采用可操作的实施就成为落实最严格水资源管理的关键。研究发现:(1)通过最严格水资源管理的压力—状态—响应(PSR)分析可知,最严格水资源管理的实施路径与水资源适应性管理在目标理念、工作途径、核心问题和保障需求方面有着一致性;(2)水资源适应性政策选择能够促进最严格水资源管理的有效落实,为此给出了适应性政策选择程序,为最严格水资源管理落实的政策选择提供参考依据。