Notes on estimation of the intraclass correlation under the AB/BA crossover trial

Under the AB/BA crossover trial, we focus our attention on estimation of the intraclass correlation in normal data. We develop both point and interval estimators in closed form for the intraclass correlation. We employ Monte Carlo simulation to study the performance of these estimators in a variety of situations. We note that the estimators developed here for the intraclass correlation remain valid even when there are possibly unexpected carry-over effects.     

审判经验的迷局与研判——从“案多人少”切入

2008年以来,江苏法院开展了大规模的总结审判经验专项活动。这是在案件数量剧增的态势下法院为化解案多人少的矛盾之为,但得到的经验相当一部分并非是有效提升办案效率的＂精品＂。从短期看,审判经验总结会适得其反,加大案多人少压力;但从长期来看,对于缓解案多人少压力会有所帮助。因此,审判经验有总结的价值,但在传承和发展审判经验时,对于个人经验和集体经验应当区别对待。     

Estimation of finite population mean in stratified sampling using scrambled responses in the presence of measurement errors

This study focuses on the estimation of population mean of a sensitive variable in stratified random sampling based on randomized response technique (RRT) when the observations are contaminated by measurement errors (ME). A generalized estimator of population mean is proposed by using additively scrambled responses for the sensitive variable. The expressions for the bias and mean square error (MSE) of the proposed estimator are derived. The performance of the proposed estimator is evaluated both theoretically and empirically. Results are also applied to a real data set.     

Bayesian sample size determination for phase IIA clinical trials using historical data and semi‐parametric prior's elicitation

The Simon's two‐stage design is the most commonly applied among multi‐stage designs in phase IIA clinical trials. It combines the sample sizes at the two stages in order to minimize either the expected or the maximum sample size. When the uncertainty about pre‐trial beliefs on the expected or desired response rate is high, a Bayesian alternative should be considered since it allows to deal with the entire distribution of the parameter of interest in a more natural way. In this setting, a crucial issue is how to construct a distribution from the available summaries to use as a clinical prior in a Bayesian design. In this work, we explore the Bayesian counterparts of the Simon's two‐stage design based on the predictive version of the single threshold design. This design requires specifying two prior distributions: the analysis prior, which is used to compute the posterior probabilities, and the design prior, which is employed to obtain the prior predictive distribution. While the usual approach is to build beta priors for carrying out a conjugate analysis, we derived both the analysis and the design distributions through linear combinations of B‐splines. The motivating example is the planning of the phase IIA two‐stage trial on anti‐HER2 DNA vaccine in breast cancer, where initial beliefs formed from elicited experts' opinions and historical data showed a high level of uncertainty. In a sample size determination problem, the impact of different priors is evaluated.     

Inference in Targeted Group‐Sequential Covariate‐Adjusted Randomized Clinical Trials

This article is devoted to the construction and asymptotic study of adaptive, group‐sequential, covariate‐adjusted randomized clinical trials analysed through the prism of the semiparametric methodology of targeted maximum likelihood estimation. We show how to build, as the data accrue group‐sequentially, a sampling design that targets a user‐supplied optimal covariate‐adjusted design. We also show how to carry out sound statistical inference based on such an adaptive sampling scheme (therefore extending some results known in the independent and identically distributed setting only so far), and how group‐sequential testing applies on top of it. The procedure is robust (i.e. consistent even if the working model is mis‐specified). A simulation study confirms the theoretical results and validates the conjecture that the procedure may also be efficient.     

Pseudo-Bayes and pseudo-empirical Bayes estimators in randomized response sampling

In this article, new pseudo-Bayes and pseudo-empirical Bayes estimators for estimating the proportion of a potentially sensitive attribute in a survey sampling have been introduced. The proposed estimators are compared with the recent estimator proposed by Odumade and Singh [Efficient use of two decks of cards in randomized response sampling, Comm. Statist. Theory Methods 38 (2009), pp. 439–446] and Warner [Randomized response: A survey technique for eliminating evasive answer bias, J. Amer. Statist. Assoc. 60 (1965), pp. 63–69].     

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.     

Time-to-event calibration-free odds design: A robust efficient design for phase I trials with late-onset outcomes

Compared with most of the existing phase I designs, the recently proposed calibration-free odds (CFO) design has been demonstrated to be robust, model-free, and easy to use in practice. However, the original CFO design cannot handle late-onset toxicities, which have been commonly encountered in phase I oncology dose-finding trials with targeted agents or immunotherapies. To account for late-onset outcomes, we extend the CFO design to its time-to-event (TITE) version, which inherits the calibration-free and model-free properties. One salient feature of CFO-type designs is to adopt game theory by competing three doses at a time, including the current dose and the two neighboring doses, while interval-based designs only use the data at the current dose and is thus less efficient. We conduct comprehensive numerical studies for the TITE-CFO design under both fixed and randomly generated scenarios. TITE-CFO shows robust and efficient performances compared with interval-based and model-based counterparts. As a conclusion, the TITE-CFO design provides robust, efficient, and easy-to-use alternatives for phase I trials when the toxicity outcome is late-onset.     

反腐败视域下监察机关证据补查的三种模式

随着纪检监察体制改革的持续深入,中国监察机关的证据补查体系逐渐呈现出退回重新调查、退回补充调查和直接补充证据三种模式。监察机关审理阶段证据补查的三种模式各有其形成的原因及运行机制,也都有独立存续的空间。退回重新调查模式和退回补充调查模式在监督制约监察机关调查权和保障案件质量方面具有一定的积极作用,但是两者容易引发内部机构之间的矛盾,进而导致案件办理效率降低。直接补充证据模式有助于提高案件的办理效率和促进内部和谐,但却模糊了监察机关内部机构之间的职责边界,容易引发制约不足而配合有余的风险。未来应当秉持法治意识、程序意识和证据意识,在保障内部监督制约和案件办理效率的基础上,严格依法适用监察机关证据补查的三种模式。     

Web-Enhanced Preservice Training for Prospective Resource Parents: A Randomized Trial of Effectiveness and User Satisfaction

ABSTRACT

Traditionally, prospective resource parents must attend all preservice training in person. Although live sessions are necessary for screening applicants, instructional portions of training could be enhanced by Web-based sessions. This pilot study compares the effectiveness of online and classroom versions of one session from a widely used preservice training program. Ninety-two individuals enrolled in the program in two states were randomly assigned to a treatment group that viewed an online version of the class on child abuse and neglect or to a comparison group that took the same class in person. Written questionnaires were completed before and after the class. Significant group differences on knowledge of child maltreatment and empathy toward birth parents plus high user satisfaction were hypothesized. Analysis of covariance results showed the online training was more effective than the live training at increasing knowledge. Multivariate analysis of covariance findings on empathy were not significant but trended toward greater empathy for the online group. Feedback indicated high satisfaction with the online course. The finding that online instruction is more effective than live instruction has positive implications for practice, because Web-based training offers advantages like standardizing instruction, cutting agency and trainee costs, and providing greater flexibility.