Robustness and Corrections for Sample Size Adaptation Strategies Based on Effect Size Estimation

A study on the robustness of the adaptation of the sample size for a phase III trial on the basis of existing phase II data is presented—when phase III is lower than phase II effect size. A criterion of clinical relevance for phase II results is applied in order to launch phase III, where data from phase II cannot be included in statistical analysis. The adaptation consists in adopting the conservative approach to sample size estimation, which takes into account the variability of phase II data. Some conservative sample size estimation strategies, Bayesian and frequentist, are compared with the calibrated optimal γ conservative strategy (viz. COS) which is the best performer when phase II and phase III effect sizes are equal. The Overall Power (OP) of these strategies and the mean square error (MSE) of their sample size estimators are computed under different scenarios, in the presence of the structural bias due to lower phase III effect size, for evaluating the robustness of the strategies. When the structural bias is quite small (i.e., the ratio of phase III to phase II effect size is greater than 0.8), and when some operating conditions for applying sample size estimation hold, COS can still provide acceptable results for planning phase III trials, even if in bias absence the OP was higher.

Main results concern the introduction of a correction, which affects just sample size estimates and not launch probabilities, for balancing the structural bias. In particular, the correction is based on a postulation of the structural bias; hence, it is more intuitive and easier to use than those based on the modification of Type I or/and Type II errors. A comparison of corrected conservative sample size estimation strategies is performed in the presence of a quite small bias. When the postulated correction is right, COS provides good OP and the lowest MSE. Moreover, the OPs of COS are even higher than those observed without bias, thanks to higher launch probability and a similar estimation performance. The structural bias can therefore be exploited for improving sample size estimation performances. When the postulated correction is smaller than necessary, COS is still the best performer, and it also works well. A higher than necessary correction should be avoided.     

The Appropriateness of Some Common Procedures for Testing the Equality of Two Independent Binomial Populations

For testing the equality of two independent binomial populations the Fisher exact test and the chi-squared test with Yates's continuity correction are often suggested for small and intermediate size samples. The use of these tests is inappropriate in that they are extremely conservative. In this article we demonstrate that, even for small samples, the uncorrected chi-squared test (i.e., the Pearson chi-squared test) and the two-independent-sample t test are robust in that their actual significance levels are usually close to or smaller than the nominal levels. We encourage the use of these latter two tests.     

A Comparison of Large-Sample Confidence Interval Methods for the Difference of Two Binomial Probabilities

A simulation study was done to compare seven confidence interval methods, based on the normal approximation, for the difference of two binomial probabilities. Cases considered included minimum expected cell sizes ranging from 2 to 15 and smallest group sizes (NMIN) ranging from 6 to 100. Our recommendation is to use a continuity correction of 1/(2 NMIN) combined with the use of (N ? 1) rather than N in the estimate of the standard error. For all of the cases considered with minimum expected cell size of at least 3, this method gave coverage probabilities close to or greater than the nominal 90% and 95%. The Yates method is also acceptable, but it is slightly more conservative. At the other extreme, the usual method (with no continuity correction) does not provide adequate coverage even at the larger sample sizes. For the 99% intervals, our recommended method and the Yates correction performed equally well and are reasonable for minimum expected cell sizes of at least 5. None of the methods performed consistently well for a minimum expected cell size of 2.     

Hypothesis test for paired samples in the presence of missing data

Missing data are present in almost all statistical analysis. In simple paired design tests, when some subject has one of the involved variables missing in the so-called partially overlapping samples scheme, it is usually discarded for the analysis. The lack of consistency between the information reported in the univariate and multivariate analysis is, perhaps, the main consequence. Although the randomness on the missing mechanism (missingness completely at random) is an usual and needed assumption for this particular situation, missing data presence could lead to serious inconsistencies on the reported conclusions. In this paper, the authors develop a simple and direct procedure which allows using the whole available information in order to perform paired tests. In particular, the proposed methodology is applied to check the equality among the means from two paired samples. In addition, the use of two different resampling techniques is also explored. Finally, real-world data are analysed.     

水基泡沫流变特性研究进展

水基泡沫在石油钻井、驱油及矿物浮选等方面应用广泛，但因其自身结构的复杂性及不稳定性，准确描述其流变性需要考虑液膜排液、气体扩散、泡沫质量、泡沫结构、可压缩性、壁面滑移、测量系统相对于气泡的尺寸、环境温度及压力等多方面因素，以致于目前尚无有关泡沫流变性的公认理论。实验研究过程中控制壁面滑移的较普遍做法是增加流道壁面（转子表面或管壁）的粗糙度，基于适当假设所提出的一些理论修正方法具有较好的适应性。体积平衡法假设泡沫管流摩擦系数为常量，在一定程度上解决了泡沫可压缩性给管流压降计算所带来的困难。目前普遍认为泡沫流变性可用幂律模式或赫谢尔–巴尔克莱模式很好地描述，是否存在“屈服应力”则因测试条件差异而存在争议。     

Bias-corrected confidence bands in nonparametric regression

Bias-corrected confidence bands for general nonparametric regression models are considered. We use local polynomial fitting to construct the confidence bands and combine the cross-validation method and the plug-in method to select the bandwidths. Related asymptotic results are obtained. Our simulations show that confidence bands constructed by local polynomial fitting have much better coverage than those constructed by using the Nadaraya–Watson estimator. The results are also applicable to nonparametric autoregressive time series models.     

青少年网瘾的心理危机分析与社会矫正

社会转型时期弱势群体的社会心理问题日益凸显,其中,青少年网络社会问题已引起普遍关注.青少年成长过程中的社会心理环境、对青少年单一的评价系统、学业和就业的巨大压力、群体内部人际交往的不适应等,都是促使部分青少年沉迷网络不能自拔的重要原因.要使青少年从根源上戒除网络游戏成瘾,摆脱由此造成的心理危机,仅靠单纯的心理辅导、正面...     

“边缘化”大学生个体成因、预防与矫治的研究

＂边缘化＂学生是指行为超出正常规范、学业严重不佳的学生个体。认为中学及大学阶段的管理、社会因素、理想与现实的错位以及个人性格心理特点等是形成＂边缘化＂学生个体的主要原因;管理者建立＂防边缘化＂预警机制、抓住管理的关键时期及努力营造良好学风是有效预防＂边缘化＂的措施;选择适当的角度和有效的工作方法可以对＂边缘化＂学生完成成功矫治。