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.     

Joint modelling of longitudinal biomarker and gap time between recurrent events: copula-based dependence

In this paper, we will extend the joint model of longitudinal biomarker and recurrent event via copula function for accounting the dependence between the two processes. The general idea of joining separate processes by allowing model-specific random effect may come from different families distribution. It is a main advantage of the proposed method that a copula construction does not constrain the choice of marginal distributions of random effects. A maximum likelihood estimation with importance sampling technique as a simple and easy understanding method is employed to model inference. To evaluate and verify the validation of the proposed joint model, a bootstrapping method as a model-based resampling is developed. Our proposed joint model is also applied to pemphigus disease data for assessing the effect of biomarker trajectory on risk of recurrence.     

开放校内外实习基地实现人才培养模式创新

开放式实习实现了学生实习与企业相结合，实习与就业相结合，实习与教师科研相结合。河南工业大学在开放式实习教学方面进行了探索性研究和实践，积累了一定经验。     

Accelerated life regression modelling of dependent bivariate time‐to‐event data

To analyze bivariate time‐to‐event data from matched or naturally paired study designs, researchers frequently use a random effect called frailty to model the dependence between within‐pair response measurements. The authors propose a computational framework for fitting dependent bivariate time‐to‐event data that combines frailty distributions and accelerated life regression models. In this framework users can choose from several parametric options for frailties, as well as the conditional distributions for within‐pair responses. The authors illustrate the flexibility that their framework represents using paired data from a study of laser photocoagulation therapy for retinopathy in diabetic patients.     

A bias analysis of Weibull models under heaped data

Retrospectively collected duration data are often reported incorrectly. An important type of such an error is heaping—respondents tend to round-off or round-up the data according to some rule of thumb. For two special cases of the Weibull model we study the behaviour of the ‘naive estimators’, which simply ignore the measurement error due to heaping, and derive closed expressions for the asymptotic bias. These results give a formal justification of empirical evidence and simulation-based findings reported in the literature. Additionally, situations where a remarkable bias has to be expected can be identified, and an exact bias correction can be performed.     

Markov random-field models for estimating local labour markets

Summary. This work is motivated by data on daily travel-to-work flows observed between pairs of elemental territorial units of an Italian region. The data were collected during the 1991 population census. The aim of the analysis is to partition the region into local labour markets. We present a new method for this which is inspired by the Bayesian texture segmentation approach. We introduce a novel Markov random-field model for the distribution of the variables that label the local labour markets for each territorial unit. Inference is performed by means of Markov chain Monte Carlo methods. The issue of model hyperparameter estimation is also addressed. We compare the results with those obtained by applying a classical method. The methodology can be applied with minor modifications to other data sets.     

Are there causal relationships between the dimensions of the Maslach Burnout Inventory? A review and two longitudinal tests

Arising from interest concerning the possibility of causal relationships among the three components of the Maslach Burnout Inventory, several process models have been proposed for the development of burnout. The present paper first reviews the evidence in favour of the three most influential of these (Leiter and Maslach's model (1988); Golembiewski, Boudreau, Munzenrider, & Luo's (1996) phase model; and Lee and Ashforth's model (1993)). These three models, and our own model (which integrates of two of them, and includes feedback effects of depersonalization on emotional exhaustion) are then compared with each other using structural equation modelling, drawing on longitudinal data from two Dutch samples (total N=1185). The review revealed that none of the seven previous studies on this issue provided any convincing support for any particular causal order proposed so far. In contrast, our own study showed that high levels of exhaustion were associated with high levels of depersonalization over time across both samples. Further, higher levels of depersonalization led to higher levels of emotional exhaustion and lower levels of personal accomplishment. To our knowledge, the present research is the first to provide reliable longitudinal evidence for the conceptualization of burnout as a developmental process, although the effects are not large enough to be of practical use in the recognition of burnout.     

Survival Analysis For A Sequenced Clinical Trial With Covariates

In this paper we discuss the survival analysis for a clinical trial in which treatment categories and general prognostic data are realised at different stages during a patient's survival time. In the light of possible strategies for the parsimonious modelling of such data, a corresponding sequence of illustrative analyses is presented. Detailed results are given for a weighted least squares analysis and these generally agree with those obtained by maximum likelihood.     

Adaptive Gaussian Markov random fields with applications in human brain mapping

Summary.  Functional magnetic resonance imaging has become a standard technology in human brain mapping. Analyses of the massive spatiotemporal functional magnetic resonance imaging data sets often focus on parametric or non-parametric modelling of the temporal component, whereas spatial smoothing is based on Gaussian kernels or random fields. A weakness of Gaussian spatial smoothing is underestimation of activation peaks or blurring of high curvature transitions between activated and non-activated regions of the brain. To improve spatial adaptivity, we introduce a class of inhomogeneous Markov random fields with stochastic interaction weights in a space-varying coefficient model. For given weights, the random field is conditionally Gaussian, but marginally it is non-Gaussian. Fully Bayesian inference, including estimation of weights and variance parameters, can be carried out through efficient Markov chain Monte Carlo simulation. Although motivated by the analysis of functional magnetic resonance imaging data, the methodological development is general and can also be used for spatial smoothing and regression analysis of areal data on irregular lattices. An application to stylized artificial data and to real functional magnetic resonance imaging data from a visual stimulation experiment demonstrates the performance of our approach in comparison with Gaussian and robustified non-Gaussian Markov random-field models.     

Testing the demand-control-support model among health-care professionals: A structural equation model

This paper reports a further empirical validation of the Demand-Control-Support Model (DCS model), which was developed by Johnson and colleagues (1988, 1989). Data were collected from a heterogeneous group of health-care professionals (nurses and nurses' aides; n = 249). Three major refinements were made to the validation of the DCS Model. First, all relationships in the model were estimated simultaneously by means of covariance structure modelling (LISREL 8). Second, the control dimension was refined substantially, using a psychometrically more sound assessment of the workers' autonomy. Third, the model was applied to the work of health-care professionals. The data did not confirm the assumption that both job strain and motivation are multiplicative functions of job demands, autonomy and social support. First, the results suggested that high levels of autonomy attenuate the increase of emotional exhaustion due to job demands. These results partially supported Karasek's Job Demand-Control Model (Karasek 1979). Second, high levels of social support proved to attenuate the increase of emotional exhaustion due to autonomy. Finally, the main effect of autonomy on job challenge implied that an increase in autonomy is accompanied by an increase in job challenge (and, consequently, job involvement). In addition, low job demands and a high amount of work-related support seem to reduce feelings of exhaustion and, consequently, health complaints.