Gender differences in predictors of late-life health insurance knowledge

For many older adults having access to affordable health care is a major concern. The present study's goal was to examine what factors were related to individuals' knowledge of late-life health insurance. A total of 131 women and 116 men (all aged 55-71) answered questions about private, Medicare, Medigap, and long-term care insurances. In addition, they answered demographic, personality, and health status questions. Results revealed that different factors are related to men's and women's knowledge of late-life health insurance options implying genderspecific educational interventions would be more effective than current educational interventions.     

Technological innovation and inequality in health

The effect of education on health has been increasing over the past several decades. We hypothesize that this increasing disparity is related to health-related technical progress: more-educated people are the first to take advantage of technological advances that improve health. We test this hypothesis using data on disease-specific mortality rates for 1980 and 1990, and cancer registry data for 1973–1993. We estimate education gradients in mortality using compulsory schooling as a measure of education. We then relate these gradients to two measures of health-related innovation: the number of active drug ingredients available to treat a disease, and the rate of change in mortality from that disease. We find that more-educated individuals have a greater survival advantage in those diseases for which there has been more health-related technological progress.     

“双循环”背景下城市房价如何影响经济增长——基于链式多重中介效应的分析

在“加快构建以国内大循环为主体、国内国际双循环相互促进的新发展格局”的背景下,我国需进一步理清房地产对投资和消费的综合影响。基于2005—2018年286个城市数据,通过构建链式多重中介效应模型和门槛效应模型,实证研究了房价对经济增长的影响效应和作用机理。研究发现：房价上涨通过投资和消费两个渠道对经济增长产生独立中介效应和链式中介效应,其中独立中介效应分别占总效应的36.12%和26.91%,链式中介效应占13.96%;区域差异较为明显,相比东部地区和西部地区,中部地区正向效应更大;房价对经济增长存在双门槛效应,房价处高位区间对经济增长作用效应趋于负向;房价对于固定资产投资具有显著积极影响,但当房地产投资与固定资产投资比值提高至一定水平,房价上涨转而抑制固定资产投资;房价处于低水平时对于消费有显著积极影响,但随着房价收入比提高,房价对消费的抑制作用越来越强。     

Convective Filmwise Condensation of R-11 inside a Vertical Tube

Partial condensation of refrigerant R-11 was experimentally and theoretically studied. The test apparatus consisted of two concentric tubes, with the vapor flowing downward in the inner tube and the cooling water in the annular space in countercurrent direction. By using the available dimensionless correlation, the mean heat transfer coefficient of the cooling water was determined, and then the mean condensationheat transfer coefficient was calculated from the overall heat transfer coefficient.The model of a homogeneous two-phase mixture of liquid and vapor, and Prandtl's principle of analogy between the momentum and heat transfer were used to establish the type of the correlation. Adopting the methods of the gradually linear regression and F-level evidence testing, the present paper proposed the dimensionless correlation to predict the relationship among the mean Nusselt number, Prandtl number, Kutateladze number and the dimensionless tube length.     

Analysis of quality-of-life adjusted failure time data in the presence of competing, possibly informative, censoring mechanisms

We derive estimators of the mean of a function of a quality-of-life adjusted failure time, in the presence of competing right censoring mechanisms. Our approach allows for the possibility that some or all of the competing censoring mechanisms are associated with the endpoint, even after adjustment for recorded prognostic factors, with the degree of residual association possibly different for distinct censoring processes. Our methods generalize from a single to many censoring processes and from ignorable to non-ignorable censoring processes.     

On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses

Statistical experiments, more commonly referred to as Monte Carlo or simulation studies, are used to study the behavior of statistical methods and measures under controlled situations. Whereas recent computing and methodological advances have permitted increased efficiency in the simulation process, known as variance reduction, such experiments remain limited by their finite nature and hence are subject to uncertainty; when a simulation is run more than once, different results are obtained. However, virtually no emphasis has been placed on reporting the uncertainty, referred to here as Monte Carlo error, associated with simulation results in the published literature, or on justifying the number of replications used. These deserve broader consideration. Here we present a series of simple and practical methods for estimating Monte Carlo error as well as determining the number of replications required to achieve a desired level of accuracy. The issues and methods are demonstrated with two simple examples, one evaluating operating characteristics of the maximum likelihood estimator for the parameters in logistic regression and the other in the context of using the bootstrap to obtain 95% confidence intervals. The results suggest that in many settings, Monte Carlo error may be more substantial than traditionally thought.     

A simple two-stage design for quantitative responses with application to a study in diabetic neuropathic pain

Two-stage designs offer substantial advantages for early phase II studies. The interim analysis following the first stage allows the study to be stopped for futility, or more positively, it might lead to early progression to the trials needed for late phase II and phase III. If the study is to continue to its second stage, then there is an opportunity for a revision of the total sample size. Two-stage designs have been implemented widely in oncology studies in which there is a single treatment arm and patient responses are binary. In this paper the case of two-arm comparative studies in which responses are quantitative is considered. This setting is common in therapeutic areas other than oncology. It will be assumed that observations are normally distributed, but that there is some doubt concerning their standard deviation, motivating the need for sample size review. The work reported has been motivated by a study in diabetic neuropathic pain, and the development of the design for that trial is described in detail.     

Models for the analysis of C-reactive protein in statin trials

Elevation in C-reactive protein (CRP) is an independent risk factor for cardiovascular disease progression and levels are reduced by treatment with statins. However, on-treatment CRP, given baseline CRP and treatment, is not normally distributed and outliers exist even when transformations are applied. Although classical non-parametric tests address some of these issues, they do not enable straightforward inclusion of covariate information. The aims of this study were to produce a model that improved efficiency and accuracy of analysis of CRP data. Estimation of treatment effects and identification of outliers were addressed using controlled trials of rosuvastatin. The robust statistical technique of MM-estimation was used to fit models to data in the presence of outliers and was compared with least-squares estimation. To develop the model, appropriate transformations of the response and baseline variables were selected. The model was used to investigate how on-treatment CRP related to baseline CRP and estimated treatment effects with rosuvastatin. On comparing least-squares and MM-estimation, MM-estimation was superior to least-squares estimation in that parameter estimates were more efficient and outliers were clearly identified. Relative reductions in CRP were higher at higher baseline CRP levels. There was also evidence of a dose-response relationship between CRP reductions from baseline and rosuvastatin. Several large outliers were identified, although there did not appear to be any relationships between the incidence of outliers and treatments. In conclusion, using robust estimation to model CRP data is superior to least-squares estimation and non-parametric tests in terms of efficiency, outlier identification and the ability to include covariate information.