生命教育视角下的农村教师生存状态问题

在生命教育的背景下,农村教师的经济状况不容乐观、身心健康不容忽视、职业倦怠不可轻视、专业发展不可怠慢。农村教师生存状态中的一系列问题主要是由社会地位的严重失落、多重压力下的职业倦怠和专业发展资源的匮乏三方面因素共同引起的。鉴于此,提出以满足农村教师的生存需要为前提,以激活农村教师的主体生命意识为保障,以促进农村教师的专业发展为核心,以提高农村教师的心理调节能力为根本,以追求农村教师的生命意义为目标,探求解决农村教师生存状态问题的途径。     

完善创业板退市制度真正建立优胜劣汰的市场机制

进一步完善创业板退市制度,健全市场退出机制,是创业板市场持续、稳定、健康发展的重要制度保障,不但有利于增强对劣质公司的威慑力和制约力,提升创业板公司整体质量,强化市场约束,净化市场环境,从而真正建立优胜劣汰的市场机制,而且有利于引导投资者牢固树立审慎的投资理念,进行更理性的投资,切实增强投资者的风险意识,警惕创业板的退市风险,从而维护投资者尤其是广大中小投资者的合法权益。通过考察海外证券市场退市制度改革的发展趋势,阐述我国创业板退市制度的主要特点,然后分析了我国证券市场现行退市制度存在的主要弊端,最后有针对性地提出了完善我国创业板退市制度、健全市场退出机制的政策建议以及相关的配套措施。     

Comparing first hitting time and proportional hazards regression models

Cox's widely used semi-parametric proportional hazards (PH) regression model places restrictions on the possible shapes of the hazard function. Models based on the first hitting time (FHT) of a stochastic process are among the alternatives and have the attractive feature of being based on a model of the underlying process. We review and compare the PH model and an FHT model based on a Wiener process which leads to an inverse Gaussian (IG) regression model. This particular model can also represent a “cured fraction” or long-term survivors. A case study of survival after coronary artery bypass grafting is used to examine the interpretation of the IG model, especially in relation to covariates that affect both of its parameters.     

MULTILEVEL DISCRETE‐TIME EVENT HISTORY MODELS WITH APPLICATIONS TO THE ANALYSIS OF RECURRENT EMPLOYMENT TRANSITIONS

Data on the timing of events such as births, residential moves and changes in employment status are collected in many longitudinal surveys. These data often have a highly complex structure, with events of several types occurring repeatedly over time to an individual and interdependences between different event processes (e.g. births and employment transitions). The aim of this paper is to review a general class of multilevel discrete‐time event history models for handling recurrent events and transitions between multiple states. It is also shown how standard methods can be extended to allow for time‐varying covariates that are outcomes of an event process that is jointly determined with the process of interest. The considerable potential of these methods for studying transitions through the life course is illustrated in analyses of the effect of the presence and age of children on women's employment transitions, using data from the British Household Panel Survey.     

Sample size re-estimation for survival data in clinical trials with an adaptive design

In clinical trials with survival data, investigators may wish to re-estimate the sample size based on the observed effect size while the trial is ongoing. Besides the inflation of the type-I error rate due to sample size re-estimation, the method for calculating the sample size in an interim analysis should be carefully considered because the data in each stage are mutually dependent in trials with survival data. Although the interim hazard estimate is commonly used to re-estimate the sample size, the estimate can sometimes be considerably higher or lower than the hypothesized hazard by chance. We propose an interim hazard ratio estimate that can be used to re-estimate the sample size under those circumstances. The proposed method was demonstrated through a simulation study and an actual clinical trial as an example. The effect of the shape parameter for the Weibull survival distribution on the sample size re-estimation is presented.     

On assessing the presence of evaluation‐time bias in progression‐free survival in randomized trials

Evaluation (or assessment)–time bias can arise in oncology trials that study progression‐free survival (PFS) when randomized groups have different patterns of timing of assessments. Modelling or computer simulation is sometimes used to explore the extent of such bias; valid results require building such simulations under realistic assumptions concerning the timing of assessments. This paper considers a trial that used a logrank test where computer simulations were based on unrealistic assumptions that severely overestimated the extent of potential bias. The paper shows that seemingly small differences in assumptions can lead to dramatic differences in the apparent operating characteristics of logrank tests. Copyright © 2010 John Wiley & Sons, Ltd.     

Model selection for two-sample problems with right-censored data: An application of Cox model

For investigating differences between two treatment groups in medical science, selecting a suitable model to capture the underlying survival function for each group with some covariates is an important issue. Many methods, such as stratified Cox model and unstratified Cox model, have been proposed for investigating the problem. However, different models generally perform differently under different circumstances and none dominates the others. In this article, we focus on two sample problems with right-censored data and propose a model selection criterion based on an approximately unbiased estimator of Kullback-Leibler loss, which accounts for estimation uncertainty in estimated survival functions obtained by various candidate models. The effectiveness of the proposed method is justified by some simulation studies and it also applied to an HIV+ data set for illustration.     

Model-free slice screening for ultrahigh-dimensional survival data

For ultrahigh-dimensional data, independent feature screening has been demonstrated both theoretically and empirically to be an effective dimension reduction method with low computational demanding. Motivated by the Buckley–James method to accommodate censoring, we propose a fused Kolmogorov–Smirnov filter to screen out the irrelevant dependent variables for ultrahigh-dimensional survival data. The proposed model-free screening method can work with many types of covariates (e.g. continuous, discrete and categorical variables) and is shown to enjoy the sure independent screening property under mild regularity conditions without requiring any moment conditions on covariates. In particular, the proposed procedure can still be powerful when covariates are strongly dependent on each other. We further develop an iterative algorithm to enhance the performance of our method while dealing with the practical situations where some covariates may be marginally unrelated but jointly related to the response. We conduct extensive simulations to evaluate the finite-sample performance of the proposed method, showing that it has favourable exhibition over the existing typical methods. As an illustration, we apply the proposed method to the diffuse large-B-cell lymphoma study.