How do policy levers shape the quality of a national health system?

Poor quality of care may have a detrimental effect on access and take-up and can become a serious barrier to the universality of health services. This consideration is of particular interest in view of the fact that health systems in many countries must address a growing public-sector deficit and respond to increasing pressures due to COVID-19 and aging population, among other factors. In line with a rapidly emerging literature, we focus on patient satisfaction as a proxy for quality of health care. Drawing on rich longitudinal and cross-sectional data for Spain and multilevel estimation techniques, we show that in addition to individual level differences, policy levers (such as public health spending and the patient-doctor ratio, in particular) exert a considerable influence on the quality of a health care system. Our results suggest that policymakers seeking to enhance the quality of care should be cautious when compromising the level of health resources, and in particular, health personnel, as a response to economic downturns in a sector that traditionally had insufficient human resources in many countries, which have become even more evident in the light of the current health crisis. Additionally, we provide evidence that the increasing reliance on the private health sector may be indicative of inefficiencies in the public system and/or the existence of features of private insurance which are deemed important by patients.     

管辂易学研究——兼论术数学的思维方式

易学可以分为两部分:理论的层面和术数的层面。学界以往对易学的术数层面关注较少，一是缘于学者对“术数”的偏见，二是术数学自身缺少系统性反思的理论大师。三国时的管辂便是试图对术数学进行理论说明的人。牟宗三曾经藉由管辂来研究中国传统的一类知识系统，但尚有许多内容待发掘。管辂说“善易者不论《易》”，这是对术数易地位的辩护，同时也是对经学易和玄学易的回应。这种反对，不能从象数和义理对立的角度去理解，而应从文本和实践的区别来认识。管辂认为，术数的根本在于“神”，这个神不是玄学或思辨意义上的“玄之又玄”，而是实践意义中的通感和知几能力，它奠基于人自身的命限和气质中。但对于神的体悟又不能仅仅等同于“直觉”和“神秘主义”，因为它对于表现的通孔———象，以及工夫论基础，都有明确的要求。在这里，我们看到了不隶属于理性、直觉或神秘主义的一种独特思维方式。     

为情而造文与为文而造情之比较研究

为情而造文与为文而造情在《文心雕龙·情采》篇中以一组对立的概念出现，关于为情而造文历来受到研究者们的肯定与重视，但为文而造情一直遭到尖锐的批判，只有个别学者加以研究与肯定。关于两者的比较研究目前尚未开展，对二者进行比较研究，有助于探索文学本位，正确对待儒家传统文化。     

Adaptive and repeated cumulative meta‐analyses of safety data during a new drug development process

During a new drug development process, it is desirable to timely detect potential safety signals. For this purpose, repeated meta‐analyses may be performed sequentially on accumulating safety data. Moreover, if the amount of safety data from the originally planned program is not enough to ensure adequate power to test a specific hypothesis (e.g., the noninferiority hypothesis of an event of interest), the total sample size may be increased by adding new studies to the program. Without appropriate adjustment, it is well known that the type I error rate will be inflated because of repeated analyses and sample size adjustment. In this paper, we discuss potential issues associated with adaptive and repeated cumulative meta‐analyses of safety data conducted during a drug development process. We consider both frequentist and Bayesian approaches. A new drug development example is used to demonstrate the application of the methods. Copyright © 2015 John Wiley & Sons, Ltd.     

Women Underrepresentation in R&I: A Sector Program Assessment of the Contribution of Gender Equality Policies in Research and Innovation

Despite decades of efforts to achieve gender equality in research and innovation (R&I), all EU member states still face remarkable difficulties in driving forward the development of their innovation system while at the same time improving gender equality by using all the available research potential. In this paper we focus on the development of the share of women researchers in four national innovation systems, i.e. in Austria, Denmark, Hungary and Spain in the time period 2005-2015. The four selected cases represent countries with significant differences in their innovation capacity, gender regimes and progress of gender equality in R&I. A qualitative comparative analysis (QCA) is carried out to conduct a sector program evaluation based on statistical data and qualitative studies to understand the dynamic development of the proportion of women researchers. The study aims to provide insights into the aggregated gender equality interventions and policies implemented in the four countries studied and their contributions to the development of the proportion of women scientists at the structural level. The analysis reveals that the development of the share of women researchers during the studied period has been particularly influenced by contextual factors, namely the relative size of the business enterprise sector and the share of women among holders of tertiary education. While this is the case, it is found that gender equality interventions need to be more widespread and more effectively designed to be a strong contributing factor to an increasing representation of women in R&I.     

A goodness of fit test for multilevel survival data

No satisfactory goodness of fit test is available for multilevel survival data which occur when survival data are clustered or hierarchical in nature. Hence the aim of this research is to develop a new goodness of fit test for multilevel survival data and to examine the properties of the newly developed test. Simulation studies were carried out to evaluate the type ? error and the power. The results showed that the type I error holds for every combination tested and that the test is powerful against the alternative hypothesis of nonproportional hazards for all combinations tested.     

Phase I monitoring and change point estimation of autocorrelated poisson regression profiles

The objective of this paper is to study the Phase I monitoring and change point estimation of autocorrelated Poisson profiles where the response values within each profile are autocorrelated. Two charts, the SLRT and the Hotelling's T², are proposed along with an algorithm for parameter estimation. The detecting power of the proposed charts is compared using simulations in terms of the signal probability criterion. The performance of the SLRT method in estimating the change point in the regression parameters is also evaluated. Moreover, a real data example is presented to illustrate the application of the methods.     

Response‐adaptive designs for binary responses: How to offer patient benefit while being robust to time trends?

Response‐adaptive randomisation (RAR) can considerably improve the chances of a successful treatment outcome for patients in a clinical trial by skewing the allocation probability towards better performing treatments as data accumulates. There is considerable interest in using RAR designs in drug development for rare diseases, where traditional designs are not either feasible or ethically questionable. In this paper, we discuss and address a major criticism levelled at RAR: namely, type I error inflation due to an unknown time trend over the course of the trial. The most common cause of this phenomenon is changes in the characteristics of recruited patients—referred to as patient drift. This is a realistic concern for clinical trials in rare diseases due to their lengthly accrual rate. We compute the type I error inflation as a function of the time trend magnitude to determine in which contexts the problem is most exacerbated. We then assess the ability of different correction methods to preserve type I error in these contexts and their performance in terms of other operating characteristics, including patient benefit and power. We make recommendations as to which correction methods are most suitable in the rare disease context for several RAR rules, differentiating between the 2‐armed and the multi‐armed case. We further propose a RAR design for multi‐armed clinical trials, which is computationally efficient and robust to several time trends considered.     

Early stopping in seamless phase I/II clinical trials

In recent years, seamless phase I/II clinical trials have drawn much attention, as they consider both toxicity and efficacy endpoints in finding an optimal dose (OD). Engaging an appropriate number of patients in a trial is a challenging task. This paper attempts a dynamic stopping rule to save resources in phase I/II trials. That is, the stopping rule aims to save patients from unnecessary toxic or subtherapeutic doses. We allow a trial to stop early when widths of the confidence intervals for the dose-response parameters become narrower or when the sample size is equal to a predefined size, whichever comes first. The simulation study of dose-response scenarios in various settings demonstrates that the proposed stopping rule can engage an appropriate number of patients. Therefore, we suggest its use in clinical trials.     

A more powerful unconditional exact test of homogeneity for 2 × c contingency table analysis

The classical unconditional exact p-value test can be used to compare two multinomial distributions with small samples. This general hypothesis requires parameter estimation under the null which makes the test severely conservative. Similar property has been observed for Fisher's exact test with Barnard and Boschloo providing distinct adjustments that produce more powerful testing approaches. In this study, we develop a novel adjustment for the conservativeness of the unconditional multinomial exact p-value test that produces nominal type I error rate and increased power in comparison to all alternative approaches. We used a large simulation study to empirically estimate the 5th percentiles of the distributions of the p-values of the exact test over a range of scenarios and implemented a regression model to predict the values for two-sample multinomial settings. Our results show that the new test is uniformly more powerful than Fisher's, Barnard's, and Boschloo's tests with gains in power as large as several hundred percent in certain scenarios. Lastly, we provide a real-life data example where the unadjusted unconditional exact test wrongly fails to reject the null hypothesis and the corrected unconditional exact test rejects the null appropriately.