Regression Analysis of Restricted Mean Survival Time Based on Pseudo-Observations

Regression models for survival data are often specified from the hazard function while classical regression analysis of quantitative outcomes focuses on the mean value (possibly after suitable transformations). Methods for regression analysis of mean survival time and the related quantity, the restricted mean survival time, are reviewed and compared to a method based on pseudo-observations. Both Monte Carlo simulations and two real data sets are studied. It is concluded that while existing methods may be superior for analysis of the mean, pseudo-observations seem well suited when the restricted mean is studied.     

Estimation of a Change Point in a Hazard Function Based on Censored Data

The hazard function plays an important role in reliability or survival studies since it describes the instantaneous risk of failure of items at a time point, given that they have not failed before. In some real life applications, abrupt changes in the hazard function are observed due to overhauls, major operations or specific maintenance activities. In such situations it is of interest to detect the location where such a change occurs and estimate the size of the change. In this paper we consider the problem of estimating a single change point in a piecewise constant hazard function when the observed variables are subject to random censoring. We suggest an estimation procedure that is based on certain structural properties and on least squares ideas. A simulation study is carried out to compare the performance of this estimator with two estimators available in the literature: an estimator based on a functional of the Nelson-Aalen estimator and a maximum likelihood estimator. The proposed least squares estimator tums out to be less biased than the other two estimators, but has a larger variance. We illustrate the estimation method on some real data sets.     

A joint modelling approach for clustered recurrent events and death events

In dental implant research studies, events such as implant complications including pain or infection may be observed recurrently before failure events, i.e. the death of implants. It is natural to assume that recurrent events and failure events are correlated to each other, since they happen on the same implant (subject) and complication times have strong effects on the implant survival time. On the other hand, each patient may have more than one implant. Therefore these recurrent events or failure events are clustered since implant complication times or failure times within the same patient (cluster) are likely to be correlated. The overall implant survival times and recurrent complication times are both interesting to us. In this paper, a joint modelling approach is proposed for modelling complication events and dental implant survival times simultaneously. The proposed method uses a frailty process to model the correlation within cluster and the correlation within subjects. We use Bayesian methods to obtain estimates of the parameters. Performance of the joint models are shown via simulation studies and data analysis.     

算子的型与指标选择

本文定义了弱*(p,q)型算子,并且通过对强(p,q)型算子指标选择结论的推广,得到了弱(p,q)型算子以及弱*(p,q)型算子指标选择的结论。     

Effects of neuroticism,extraversion, circadian type and age on reported driver stress

Studies have examined effects of various personality variables, including extraversion and neuroticism, as well as age, on driver stress. However, the effect of the morningness-eveningness dimension (circadian type) on reported driver stress among commuters has not been investigated. This study aimed to assess the influence of circadian type as well as extraversion, neuroticism and age on driver stress. Participants were 101 Australian university administrative staff who completed the Eysenck Personality Questionnaire-Revised, the Morningness-Eveningness Questionnaire, and also morning and evening driving diaries as a state measure of driver stress each weekday for one week. Two standard multiple regressions assessed whether neuroticism, extraversion, circadian type and age predicted reported driver stress for morning and evening drives. Neuroticism, circadian type, and age predicted reported driver stress in the mornings but age mainly predicted driver stress in the evenings. A repeated measures ANOVA isolated differences between circadian types for weekday mornings and evenings. Reported driver stress differed for day of week and time of day according to circadian type. The three-way interaction suggested that driver stress is influenced by circadian type and that factors influence driver stress differentially between mornings and evenings. Circadian type appears to influence when driver stress affects individuals.     

Inference Under Right Censoring in a Discrete Setup

In this article, we consider the right random censoring scheme in a discrete setup when the lifetime and censoring variables are independent and have geometric distributions with means 1/θ₁ and 1/θ₂, respectively. We first obtain the Maximum Likelihood and Method of Moment estimators of the unknown parameters. We also find the Bayes and Posterior Regret Gamma Minimax estimators of the parameters for the two cases when the prior distributions are dependent and independent, assuming a squared error loss function. We then discuss the Proportional Hazard model, and obtain Maximum Likelihood estimators of the unknown parameters and derive the Bayes estimators assuming squared error loss using Markov Chain Monte Carlo methods.     

安乐死的分类——一个讨论的进路

安乐死问题上聚讼纷纭的不同声音，交织了道德的纷争与政策上的考量。因而适度的区分道德与法律问题是必要的，这其中安乐死的分类成为讨论安乐死问题的一种策略和分析路径。尽管各种分类的道德差异尚不清晰，但其中的一些基础分类已为国外的法律和司法判决所支持，成为探讨安乐死的正当性并逐步获取道德共识的进路，也为法律和公共政策谨慎处理安乐死事案提供了可行的策略。     

Exact inference for a simple step-stress model with competing risks for failure from exponential distribution under Type-II censoring

In reliability analysis, accelerated life-testing allows for gradual increment of stress levels on test units during an experiment. In a special class of accelerated life tests known as step-stress tests, the stress levels increase discretely at pre-fixed time points, and this allows the experimenter to obtain information on the parameters of the lifetime distributions more quickly than under normal operating conditions. Moreover, when a test unit fails, there are often more than one fatal cause for the failure, such as mechanical or electrical. In this article, we consider the simple step-stress model under Type-II censoring when the lifetime distributions of the different risk factors are independently exponentially distributed. Under this setup, we derive the maximum likelihood estimators (MLEs) of the unknown mean parameters of the different causes under the assumption of a cumulative exposure model. The exact distributions of the MLEs of the parameters are then derived through the use of conditional moment generating functions. Using these exact distributions as well as the asymptotic distributions and the parametric bootstrap method, we discuss the construction of confidence intervals for the parameters and assess their performance through Monte Carlo simulations. Finally, we illustrate the methods of inference discussed here with an example.     

立式盘类转子动平衡机箱体有限元分析

旋转电极式电除尘器出口粉尘体积分数小,可降低PM2.5排放量,是目前新建和改造电除尘器的主流方向之 一,其清灰刷关系到旋转电极式电除尘器的除尘效率和使用寿命,为此搭建了清灰刷失效形式和寿命测试试验装置,其 中机械部分为框架结构,电气部分采用PLC控制,由减速变频电机驱动,最后通过ANSYS Workbench对其主要部件进行 了静态和动态性能分析,结果表明：装置框架的动态特性满足设计要求,不会在工作时引起共振,运行后该装置能够满足 试验工作需要。     

Flexible modelling of simultaneously interval censored and truncated time‐to‐event data

This paper deals with the analysis of data from a HET‐CAM^VT experiment. From a statistical perspective, such data yield many challenges. First of all, the data are typically time‐to‐event like data, which are at the same time interval censored and right truncated. In addition, one has to cope with overdispersion as well as clustering. Traditional analysis approaches ignore overdispersion and clustering and summarize the data into a continuous score that can be analysed using simple linear models. In this paper, a novel combined frailty model is developed that simultaneously captures all of the aforementioned statistical challenges posed by the data. Copyright © 2015 John Wiley & Sons, Ltd.