Seamless phase II/III/IIIb clinical trial designs with different endpoints for different phases

Abstract

In a seamless phase II/III/IIIb trial, K (K?≥?2) doses versus placebo control are evaluated at phase II. Based on phase II results, one dose will be selected for phases III and IIIb. Pre-specified additional numbers of patients will be enrolled into the selected dose and placebo control during phases III and IIIb. Results of the phase III endpoint may be submitted for an early New Drug Application. Final analyses will be conducted for ultimate claims of treatment effects for the selected dose on the phase III and IIIb endpoints. Multiplicity adjustment is performed for the overall type I error rate control.     

A systemic approach to sociometric group research: Advancing the work of Leslie Day Zeleny, 1939-1947

This paper contributes to the exploration and analysis of compatibility and diversity across multiple, interrelated, structural configurations of groups geared toward the attainment of some objective requiring cooperative activity. The systemic nature of such contexts is addressed through a requisite approach that conforms to sociometric principles and methodological and measurement standards. Indexical properties, formulations, and computations are discussed in detail, and the provision of sample interpretations, along with an extensive array of visuals, fosters informed implementation. A classic data set is tackled with precision regarding its conception, construction and assimilation. The questions that this data set raises proffer innovative computational advancements, serving to promote it as one of the archetypes to be used in the teaching and research of social network analysis.     

Maximum entropy extreme‐value seasonal adjustment

Some economic series in small economies exhibit meagre (i.e. non‐positive) values, as well as seasonal extremes. For example, agricultural variables in countries with a distinct growing season may exhibit both of these features. Multiplicative seasonal adjustment typically utilises a logarithmic transformation, but the meagre values make this impossible, while the extremes engender huge distortions that render seasonal adjustments unacceptable. To account for these features, we propose a new method of extreme‐value adjustment based on the maximum entropy principle, which results in replacement of the meagre values and extremes by optimal projections that utilise information from the available time series dynamics. This facilitates multiplicative seasonal adjustment. The method is illustrated in the New Zealand agricultural series.     

Bhutanese refugee adolescents’ school adjustment: the role of acculturation and familial social capital

With the rise of conflict, persecution, and generalised violence worldwide there has been a drastic increase in the number of refugee youth displaced globally. Particularly noteworthy is the recent increase of Bhutanese refugee youth populations resettled in the United States. Despite this increase, surprisingly few studies distinctly address the promotive factors related to refugee youth’s successful adjustment within their new school settings upon resettlement. This study aims to add to the scholarship through a resiliency lens. The potential promotive associations of familial social capital and acculturation identity on Bhutanese refugee youth’s successful school adjustment were explored. Moreover, this study examined the unique additive predictive association of familial social capital on Bhutanese refugee youth’s school adjustment over and above the effects of acculturation. The results indicate a significant additive positive relationship between familial social capital and school adjustment, beyond the effects of ethnic identity.     

Active emotion regulation mediates links between shyness and social adjustment in preschool

Shyness is characterized by the experience of heightened fear, anxiety, and social‐evaluative concerns in social situations and is associated with increased risk for social adjustment difficulties. Previous research suggests that shy children have difficulty regulating negative emotions, such as anger and disappointment, which contributes to problems interacting with others. However, it remains unclear precisely which strategies are involved among these associations. Accordingly, the goal of this study was to explore the mediating role of emotion regulation strategies in the links between young children’s shyness and social adjustment at preschool. Participants were 248 preschool children aged 2.5–5 years. Parents rated children’s shyness and emotion regulation strategies in the context of anger and fear. Early childhood educators assessed indices of social adjustment 4 months later. Among the results, active regulation mediated associations between shyness and subsequent prosocial and socially withdrawn behaviors. Child gender further moderated these linkages, such that the model predicting socially withdrawn behavior was stronger among boys. These results expand on our understanding of emotion regulation strategies in shy children’s early socio‐emotional development.     

Weighing Evidence “Steampunk” Style via the Meta-Analyser

The funnel plot is a graphical visualization of summary data estimates from a meta-analysis, and is a useful tool for detecting departures from the standard modeling assumptions. Although perhaps not widely appreciated, a simple extension of the funnel plot can help to facilitate an intuitive interpretation of the mathematics underlying a meta-analysis at a more fundamental level, by equating it to determining the center of mass of a physical system. We used this analogy to explain the concepts of weighing evidence and of biased evidence to a young audience at the Cambridge Science Festival, without recourse to precise definitions or statistical formulas and with a little help from Sherlock Holmes! Following on from the science fair, we have developed an interactive web-application (named the Meta-Analyser) to bring these ideas to a wider audience. We envisage that our application will be a useful tool for researchers when interpreting their data. First, to facilitate a simple understanding of fixed and random effects modeling approaches; second, to assess the importance of outliers; and third, to show the impact of adjusting for small study bias. This final aim is realized by introducing a novel graphical interpretation of the well-known method of Egger regression.     

Methodology for stochastic volatility process calibration application to the CAC 40 index

In considering volatility as a stochastic, the aim of this paper is to estimate the four parameters related to a particular stochastic process named P1 and based on a Wiener–Levy process. We present the methodology to estimate its four parameters. We calibrate this theoretical model P1 to the CAC 40 index real data. In the same time, we test the normality of the random variables related to the two Wiener–Levy processes. The calibration is performed using the implemented aforesaid algorithm. We compare the stochastic process P1 with another process named P2 and to the Heston [Closed form solution for options with stochastic volatility with application to bonds and currency options, Rev. Financ. Stud. 6(2) (1993), pp. 327–343] process named H0 and to two other improved Heston processes named H1 and H2. For the empirical study, the same algorithm is used to calibrate the five processes. The calibration is based on a database including the CAC 40 index daily ‘closing fixing’ values for the time period from 3rd January 2005 to 22nd January 2007. The data are divided into 18 classes relative to 18 different contracts of European calls on the CAC 40 index. As a result, we find that, the normality test of the CAC 40 index is rejected which is in accordance with the previous original works dealing with this problem. For the five volatility processes, the normality test is verified almost for the same contracts. We also find that according to the used data, the process P1 and its equivalent H1 are the best for calibration.     

Asymptotic Comparison of Two Discriminants Used in Normal Covariate Classification

In this paper, we examine the performance of Anderson's classification statistic with covariate adjustment in comparison with the usual Anderson's classification statistic without covariate adjustment in a two-population normal covariate classification problem. The same problem has been investigated using different methods of comparison by some authors. See the bibliography. The aim of this paper is to give a direct comparison based upon the asymptotic probabilities of misclassification. It is shown that for large equal sample size of a training sample from each population, Anderson's classification statistic with covariate adjustment and cut-off point equal to zero, has better performance.     

On an adjustment of degrees of freedom in the minimim mean squared error ertimator

In this paper, we consider an adjustment of degrees of freedom in the minimum mean squared error (MMSE) estimator, We derive the exact MSE of the adjusted MMSE (AMMSE) estimator, and compare the MSE of the AMMSE estimator with those of the Stein-(SR), positive-part Stein-rule (PSR) and MMSE estimators by numerical evaluations. It is shown that the adjustment of degrees of freedom is effective when the noncentrality parameter is close to zero, and the MSE performance of the MMSE estimator can be improved in the wide region of the noncentrality parameter by the adjustment, ft is also shown that the AMMSE estimator can have the smaller MSE than the PSR estimator in the wide region of the noncentrality parameter