Vaccine clinical trials with dynamic borrowing of historical controls: Two retrospective studies

Traditional vaccine efficacy trials usually use fixed designs with fairly large sample sizes. Recruiting a large number of subjects requires longer time and higher costs. Furthermore, vaccine developers are more than ever facing the need to accelerate vaccine development to fulfill the public's medical needs. A possible approach to accelerate development is to use the method of dynamic borrowing of historical controls in clinical trials. In this paper, we evaluate the feasibility and the performance of this approach in vaccine development by retrospectively analyzing two real vaccine studies: a relatively small immunological trial (typical early phase study) and a large vaccine efficacy trial (typical Phase 3 study) assessing prophylactic human papillomavirus vaccine. Results are promising, particularly for early development immunological studies, where the adaptive design is feasible, and control of type I error is less relevant.     

缩小城乡收入差距哪种社保类型更有效？——基于财政社保与社会保险的比较分析

党的十九届五中全会将共同富裕纳入2035年远景目标,社会保障具有较强的针对性和直接性,是缩小城乡收入差距、实现共同富裕的重要手段。基于此,运用2009—2019年全国31个省级行政区划单位的面板数据,基于包含财政社保水平和社会保险水平的数理模型分析了二者对城乡收入差距的影响,通过空间相关性和非线性假设对问题展开研究。研究表明,省际城乡收入差距具有正的空间相关性,财政社保水平可以有效缩小城乡收入差距,并且随着劳动力市场化进程,其政策效果将会逐步凸显。社会保险水平缩小城乡收入差距的作用较弱,主要原因在于养老保险制度难以适配劳动力市场化进程,而医疗保险和其他社会保险水平则相反。基于以上研究,我国应当增加财政社保支出,因地制宜地调整社会保险制度,形成缩小城乡收入差距的合力。     

函数型神经网络在捷联陀螺非线性估计中的应用

针对神经网络的强自学习性、自适应能力及非线性变换特性,结合陀螺静态漂移误差模型,采用函数型神经网络对捷联陀螺静态漂移误差系数进行了非线性估计,解决了捷联陀螺重复启动时的静态漂移误差系数的在线动态标定问题.     

Asymptotic Distribution Theory for Nonparametric Entropy Measures of Serial Dependence

Entropy is a classical statistical concept with appealing properties. Establishing asymptotic distribution theory for smoothed nonparametric entropy measures of dependence has so far proved challenging. In this paper, we develop an asymptotic theory for a class of kernel‐based smoothed nonparametric entropy measures of serial dependence in a time‐series context. We use this theory to derive the limiting distribution of Granger and Lin's (1994) normalized entropy measure of serial dependence, which was previously not available in the literature. We also apply our theory to construct a new entropy‐based test for serial dependence, providing an alternative to Robinson's (1991) approach. To obtain accurate inferences, we propose and justify a consistent smoothed bootstrap procedure. The naive bootstrap is not consistent for our test. Our test is useful in, for example, testing the random walk hypothesis, evaluating density forecasts, and identifying important lags of a time series. It is asymptotically locally more powerful than Robinson's (1991) test, as is confirmed in our simulation. An application to the daily S&P 500 stock price index illustrates our approach.