Sample sizes for trials involving multiple correlated must-win comparisons

In Clinical trials involving multiple comparisons of interest, the importance of controlling the trial Type I error is well-understood and well-documented. Moreover, when these comparisons are themselves correlated, methodologies exist for accounting for the correlation in the trial design, when calculating the trial significance levels. However, less well-documented is the fact that there are some circumstances where multiple comparisons affect the Type II error rather than the Type I error, and failure to account for this, can result in a reduction in the overall trial power. In this paper, we describe sample size calculations for clinical trials involving multiple correlated comparisons, where all the comparisons must be statistically significant for the trial to provide evidence of effect, and show how such calculations have to account for multiplicity in the Type II error. For the situation of two comparisons, we provide a result which assumes a bivariate Normal distribution. For the general case of two or more comparisons we provide a solution using inflation factors to increase the sample size relative to the case of a single outcome. We begin with a simple case of two comparisons assuming a bivariate Normal distribution, show how to factor in correlation between comparisons and then generalise our findings to situations with two or more comparisons. These methods are easy to apply, and we demonstrate how accounting for the multiplicity in the Type II error leads, at most, to modest increases in the sample size.     

Escalation strategies for combination therapy Phase I trials

Phase I clinical trials aim to identify a maximum tolerated dose (MTD), the highest possible dose that does not cause an unacceptable amount of toxicity in the patients. In trials of combination therapies, however, many different dose combinations may have a similar probability of causing a dose‐limiting toxicity, and hence, a number of MTDs may exist. Furthermore, escalation strategies in combination trials are more complex, with possible escalation/de‐escalation of either or both drugs. This paper investigates the properties of two existing proposed Bayesian adaptive models for combination therapy dose‐escalation when a number of different escalation strategies are applied. We assess operating characteristics through a series of simulation studies and show that strategies that only allow ‘non‐diagonal’ moves in the escalation process (that is, both drugs cannot increase simultaneously) are inefficient and identify fewer MTDs for Phase II comparisons. Such strategies tend to escalate a single agent first while keeping the other agent fixed, which can be a severe restriction when exploring dose surfaces using a limited sample size. Meanwhile, escalation designs based on Bayesian D‐optimality allow more varied experimentation around the dose space and, consequently, are better at identifying more MTDs. We argue that for Phase I combination trials it is sensible to take forward a number of identified MTDs for Phase II experimentation so that their efficacy can be directly compared. Researchers, therefore, need to carefully consider the escalation strategy and model that best allows the identification of these MTDs. Copyright © 2012 John Wiley & Sons, Ltd.     

A bivariate Bayesian dose‐finding procedure applied to a seamless phase I/II trial in rheumatoid arthritis

We describe a dose escalation procedure for a combined phase I/II clinical trial. The procedure is based on a Bayesian model for the joint distribution of the occurrence of a dose limiting event and of some indicator of efficacy (both considered binary variables), making no assumptions other than monotonicity. Thus, the chances of each outcome are assumed to be non‐decreasing in dose level. We applied the procedure to the design of a placebo‐controlled, sequential trial in rheumatoid arthritis, in each stage of which patients were randomized between placebo and all dose levels that currently appeared safe and non‐futile. On the basis of data from a pilot study, we constructed five different scenarios for the dose–response relationships under which we simulated the trial and assessed the performance of the procedure. The new design appears to have satisfactory operating characteristics and can be adapted to the requirements of a range of trial situations. Copyright © 2012 John Wiley & Sons, Ltd.     

上海市对“支出型”贫困群体的综合帮扶研究

上海市民政局首次提出"支出型贫困",表明官方甄别穷人的方式发生改变。上海市建立市民收入核对系统,探索社区居民综合帮扶机制;深化分类施保,实行"保基本、可叠加、多组合"的救助套餐;健全就业、保险、救助和慈善的"四环联动",与各类社会保障制度无缝衔接。上海市民政部门还在实践中创建出几种综合帮扶模式,如长宁区的"四医联动"基本医疗保障模式,虹口区的四项帮扶机制,徐汇区的"五可"帮困救助运作方式。     

Macroeconomic and financing determinants of out of pocket payments in health care: Evidence from selected OECD countries

In this paper, we examine the impact of macroeconomic, as well public and private health insurance financing (PHI) factors on out of pocket (OOP) healthcare expenditures, by using fixed/random effects and dynamic panel data methodology to a dataset of 26 EU and OECD countries for a period lasting from 1995 to 2013. The existing empirical literature has focused on testing the hypothesis that several macroeconomic and health financing determinants have an effect on OOP healthcare expenditures. Nevertheless, the related articles have not well tested the hypothesis concerning the potential impact of PHI financing on OOP spending. We find that public and PHI financing have a significant countervailing effect on OOP spending. Moreover, we show that unemployment rate has a significant positive impact on OOP expenditures. Sensitivity tests with variation of specifications and samples show that our findings are robust. We argue that policy-makers should give serious consideration to PHI institution; our results indicate that there is an inverse effect on OOP spending. We suggest that our examined countries have to provide financial risk protection to their citizens against OOP payments, rather than only attending health budgetary retrenchments in order to adjust public finances.     

Progressively Type-II censored competing risks data for exponential distributions based on sequential order statistics

We consider the progressively Type-II censored competing risks model based on sequential order statistics. It is assumed that the latent failure times are independent and the failure of each unit influences the lifetime distributions of the latent failure times of surviving units. We provide explicit expressions for the likelihood function of the available data under the conditional proportional hazard rate (CPHR) and the power trend conditional proportional hazard rate (PTCPHR) models. Under CPHR and PTCPHR models and assumption that the baseline distributions of the latent failure times are exponential, classical and Bayesian estimates of the unknown parameters are provided. Monte Carlo simulations are then performed for illustrative purposes. Finally, two datasets are analyzed.     

Survival Weibull regression model for mismeasured outcomes

In some survival studies, the exact time of the event of interest is unknown, but the event is known to have occurred during a particular period of time (interval-censored data). If the diagnostic tool used to detect the event of interest is not perfectly sensitive and specific, outcomes may be mismeasured; a healthy subject may be diagnosed as sick and a sick one may be diagnosed as healthy. In such cases, traditional survival analysis methods produce biased estimates for the time-to-failure distribution parameters (Paggiaro and Torelli 2004 Paggiaro, A., and N. Torelli. 2004. The effect of classification errors in survival data analysis. Statistical Methods and Applications 13:213–25.[Crossref] , [Google Scholar]). In this context, we developed a parametric model that incorporates sensitivity and specificity into a grouped survival data analysis (a case of interval-censored data in which all subjects are tested at the same predetermined time points). Inferential aspects and properties of the methodology, such as the likelihood function and identifiability, are discussed in this article. Assuming known and non differential misclassification, Monte Carlo simulations showed that the proposed model performed well in the case of mismeasured outcomes; the estimates of the relative bias of the model were lower than those provided by the naive method that assumes perfect sensitivity and specificity. The proposed methodology is illustrated by a study related to mango tree lifetimes.     

组织学习与知识转化的关系及IT对二者影响的探讨

基于组织学习与知识管理理论,将组织学习过程的4I模型与知识创新的SECI模型进行整合与扩展,探讨了组织学习的个体、团队及组织3个层次与隐性知识与显性知识2类知识形式的对应关系,以及组织学习过程与隐性-显性知识相互转化过程的动态匹配关系.与以往研究中将知识转移、共享与扩散作为一个知识的单向传递过程所不同的是,认为知识转化是一个螺旋上升的循环过程,是包含知识创新的持续学习过程,而IT将促进组织学习与知识转化的结合.最后,通过一个案例分析以展示IT对企业组织学习与知识转化的积极影响.     

试析早期谢林与费希特的“绝对自我”观的差异

学界通常认为,费希特和谢林（在前者的影响下）都是在其哲学的起步阶段就建立了一种关于“绝对自我”的学说。本文试图表明,费希特在《全部知识学的基础》中从“经验意识的事实”出发,并没有真正建立绝对自我;反之,谢林的原创性体现在,他从一开始就依据概念分析而确立了自我的绝对者地位,从而真正第一次建立起关于“绝对自我”的学说。     

Testing whether new is better than used of a specified age,with randomly censored data

Using randomly censored data, we develop a test of the null hypothesis that a new item has stochastically the same residual lifelength as does a used item of specified age t₀, versus the alternative hypothesis that a new item has stochastically greater residual lifelength than does a used item of age t₀. We also compare our test with a related test, developed for a complete-data model, in order to study the loss in efficiency because of censoring.