A comparison of nonparametric priors in hierarchical mixture modelling for AFT regression

We will pursue a Bayesian nonparametric approach in the hierarchical mixture modelling of lifetime data in two situations: density estimation, when the distribution is a mixture of parametric densities with a nonparametric mixing measure, and accelerated failure time (AFT) regression modelling, when the same type of mixture is used for the distribution of the error term. The Dirichlet process is a popular choice for the mixing measure, yielding a Dirichlet process mixture model for the error; as an alternative, we also allow the mixing measure to be equal to a normalized inverse-Gaussian prior, built from normalized inverse-Gaussian finite dimensional distributions, as recently proposed in the literature. Markov chain Monte Carlo techniques will be used to estimate the predictive distribution of the survival time, along with the posterior distribution of the regression parameters. A comparison between the two models will be carried out on the grounds of their predictive power and their ability to identify the number of components in a given mixture density.     

Copula-based regression models: A survey

In this review paper we collect several results about copula-based models, especially concerning regression models, by focusing on some insurance applications.     

Nonparametric estimation of waiting time distributions in a Markov model based on current status data

We construct nonparametric estimators of state waiting time distribution functions in a Markov multistate model using current status data. This is a particularly difficult problem since neither the entry nor the exit times of a given state are directly observed. These estimators are obtained, using the Markov property, from estimators of counting processes of state entry and exit times, as well as, the size of “at risk” sets of state entry and transitions out of that state. Consistency of our estimators is established. Finite-sample behavior of our estimators is studied by simulation, in which we show that our estimators based on current status data compare well with those based on complete data. We also illustrate our method using a pubertal development data set obtained from the NHANES III [1997. NHANES III Reference Manuals and Reports (CD-ROM). Analytic and Reporting Guidelines: The Third National Health and Nutrition Examination Survey (1988–94). National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD] study.     

我国消费、投资、净出口与经济增长

 消费、投资与出口通常并称为拉动经济增长的三驾马车,对经济增长产生重要作用。同时,经济增长也影响着它们的规模。针对这种情况,本文通过建立联立方程模型以及脉冲响应函数,对我国经济1978-2006年的历史数据进行实证分析,揭示我国改革开放以来它们之间的关系。     

A re-evaluation of random-effects meta-analysis

Summary.  Meta-analysis in the presence of unexplained heterogeneity is frequently undertaken by using a random-effects model, in which the effects underlying different studies are assumed to be drawn from a normal distribution. Here we discuss the justification and interpretation of such models, by addressing in turn the aims of estimation, prediction and hypothesis testing. A particular issue that we consider is the distinction between inference on the mean of the random-effects distribution and inference on the whole distribution. We suggest that random-effects meta-analyses as currently conducted often fail to provide the key results, and we investigate the extent to which distribution-free, classical and Bayesian approaches can provide satisfactory methods. We conclude that the Bayesian approach has the advantage of naturally allowing for full uncertainty, especially for prediction. However, it is not without problems, including computational intensity and sensitivity to a priori judgements. We propose a simple prediction interval for classical meta-analysis and offer extensions to standard practice of Bayesian meta-analysis, making use of an example of studies of 'set shifting' ability in people with eating disorders.     

A flexible semiparametric transformation model for survival data

I suggest an extension of the semiparametric transformation model that specifies a time-varying regression structure for the transformation, and thus allows time-varying structure in the data. Special cases include a stratified version of the usual semiparametric transformation model. The model can be thought of as specifying a first order Taylor expansion of a completely flexible baseline. Large sample properties are derived and estimators of the asymptotic variances of the regression coefficients are given. The method is illustrated by a worked example and a small simulation study. A goodness of fit procedure for testing if the regression effects lead to a satisfactory fit is also suggested.     

On the use of quasi-likelihood for estimation in hidden-Markov random fields

This paper illustrates the use of quasi-likelihood methods of inference for hidden Markov random fields. These are simple to use and can be employed under circumstances where only the model form and its covariance structure are specified. In particular they can be used to derive the same estimating equations as the E-M algorithm or change of measure methods, which make full distributional assumptions.     

Housing interventions for homeless,pregnant/parenting women with addictions: a systematic review

Family homelessness is a growing problem in North America with most of these families headed by single women. Homeless women also experience high rates of pregnancy and addiction (drugs and alcohol). Housing interventions have been identified as key to addressing the complex needs of pregnant/early parenting, homeless women with addictions. The aim of this systematic review is to determine what housing models and programs for this population yield the best outcomes. We systematically searched 10 databases and retrieved eight articles describing four distinct studies that met the inclusion criteria for this review. Overall, improved outcomes were found for all intervention groups with the most recent and rigorous studies favoring models combining Housing First and case management. However, methodological limitations, study quality, and varied outcomes made comparison across studies difficult. Further research must be done in this area using standardized outcomes and rigorous designs to develop evidence-based best practice guidelines to address the unique needs of this population.     

The time‐dependent “cure‐death” model investigating two equally important endpoints simultaneously in trials treating high‐risk patients with resistant pathogens

A variety of primary endpoints are used in clinical trials treating patients with severe infectious diseases, and existing guidelines do not provide a consistent recommendation. We propose to study simultaneously two primary endpoints, cure and death, in a comprehensive multistate cure‐death model as starting point for a treatment comparison. This technique enables us to study the temporal dynamic of the patient‐relevant probability to be cured and alive. We describe and compare traditional and innovative methods suitable for a treatment comparison based on this model. Traditional analyses using risk differences focus on one prespecified timepoint only. A restricted logrank‐based test of treatment effect is sensitive to ordered categories of responses and integrates information on duration of response. The pseudo‐value regression provides a direct regression model for examination of treatment effect via difference in transition probabilities. Applied to a topical real data example and simulation scenarios, we demonstrate advantages and limitations and provide an insight into how these methods can handle different kinds of treatment imbalances. The cure‐death model provides a suitable framework to gain a better understanding of how a new treatment influences the time‐dynamic cure and death process. This might help the future planning of randomised clinical trials, sample size calculations, and data analyses.