Estimation of dependence between paired correlated failure times in the presence of covariate measurement error

Summary. In many biomedical studies, covariates are subject to measurement error. Although it is well known that the regression coefficients estimators can be substantially biased if the measurement error is not accommodated, there has been little study of the effect of covariate measurement error on the estimation of the dependence between bivariate failure times. We show that the dependence parameter estimator in the Clayton–Oakes model can be considerably biased if the measurement error in the covariate is not accommodated. In contrast with the typical bias towards the null for marginal regression coefficients, the dependence parameter can be biased in either direction. We introduce a bias reduction technique for the bivariate survival function in copula models while assuming an additive measurement error model and replicated measurement for the covariates, and we study the large and small sample properties of the dependence parameter estimator proposed.     

Bayesian inference for a discretely observed stochastic kinetic model

The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observed by means of discrete-time sampling systems, which are often limited in their completeness. In this paper we explore how to make Bayesian inference for the kinetic rate constants of regulatory networks, using the stochastic kinetic Lotka-Volterra system as a model. This simple model describes behaviour typical of many biochemical networks which exhibit auto-regulatory behaviour. Various MCMC algorithms are described and their performance evaluated in several data-poor scenarios. An algorithm based on an approximating process is shown to be particularly efficient.     

A graphical summary of disparities in health care and related summary measures

In order to assess the effectiveness of government programs designed to reduce disparities in the health care minority groups receive relative to the majority white population, a proper statistical measure should be used. This article proposes a measure of and its accompanying graph, which is readily interpretable and is not affected by the number of minority subgroups examined.     

Almost Consistent Estimation of Panel Probit Models with “Small” Fixed Effects

We propose four different GMM estimators that allow almost consistent estimation of the structural parameters of panel probit models with fixed effects for the case of small Tand large N. The moments used are derived for each period from a first order approximation of the mean of the dependent variable conditional on explanatory variables and on the fixed effect. The estimators differ w.r.t. the choice of instruments and whether they use trimming to reduce the bias or not. In a Monte Carlo study, we compare these estimators with pooled probit and conditional logit estimators for different data generating processes. The results show that the proposed estimators outperform these competitors in several situations.     

Trait Expressiveness and Marital Satisfaction: The Role of Idealization Processes

This study examined the processes that underlie the association between trait expressiveness and marital satisfaction. A total of 168 newlywed couples participated in a four‐wave, 13‐year longitudinal study of marriage. Cross‐sectional and longitudinal path analyses suggested that expressiveness promotes satisfaction by leading spouses to engage in affectionate behavior and by leading them to idealize their partner. Expressive people formed idealized images of their partner because they brought out the best in their partner's behavior and because they interpreted their partner's behavior in a favorable light. The study extends previous research by showing that the benefits of trait expressiveness extend into the second decade of marriage and by providing a plausible explanation of the connections between trait expressiveness and marital satisfaction.     

The role of ethics in interdisciplinary technology assessment

Technology Assessment (TA) is a problem oriented endeavour dealing with political, societal, ecological, etc. problems. Only in rare cases is one individual scientific discipline sufficient to assess these problems. Usually the perspectives of different scientific disciplines have to be combined in order to develop interdisciplinary based recommendations to act. In this paper a quality controlled interdisciplinary discussion process is described which encourages an expert group to generate argumentation chains cross-cutting the disciplinary boundaries. The role of ethical reflection in this procedure depends on the problem situation. Whenever a technical application is on the agenda which cannot be allocated to a so-called “business-as-usual” case, one would ask for ethical reflection. This contribution argues that this ethical reflection has to take place together with the interdisciplinary discussion due to two reasons. Firstly, the technical, economical, legal and social aspects are deeply cross-correlated with the ethical reflection. And secondly, participating in such interdisciplinary discussions enables an ethical reflection which keeps in touch with the real world. Two case studies dealing with robotics applications in health care are mentioned as examples for problem settings, in which interdisciplinary TA succeed in developing discipline-crossing argumentation chains.     

Reconsidering the Evolution of Nonlinguistic Communication: The Case of Laughter

Nonlinguistic communication is typically proposed to convey representational messages, implying that particular signals are associated with specific signaler emotions, intentions, or external referents. However, common signals produced by both nonhuman primates and humans may not exhibit such specificity, with human laughter for example showing significant diversity in both acoustic form and production context. We therefore outline an alternative to the representational approach, arguing that laughter and other nonlinguistic vocalizations are used to influence the affective states of listeners, thereby also affecting their behavior. In the case of laughter, we propose a primary function of accentuating or inducing positive affect in the perceiver in order to promote a more favorable stance toward the laugher. Two simple strategies are identified, namely producing laughter with acoustic features that have an immediate impact on listener arousal, and pairing these sounds with positive affect in the listener to create learned affective responses. Both depend on factors like the listener's current emotional state and past interactions with the vocalizer, with laughers predicted to adjust their sounds accordingly. This approach is used to explain findings from two experimental studies that examined the use of laughter in same-sex and different-sex dyads composed of either friends or strangers, and may be applicable to other forms of nonlinguistic communication.     

A Bayesian Hierarchical Model for the Evaluation of a Website

Consider a website and the surfers visiting its pages. A typical issue of interest, for example while monitoring an advertising campaign, concerns whether a specific page has been designed successfully, i.e. is able to attract surfers or address them to other pages within the site. We assume that the surfing behaviour is fully described by the transition probabilities from one page to another, so that a clickstream (sequence of consecutively visited pages) can be viewed as a finite-state-space Markov chain. We then implement a variety of hierarchical prior distributions on the multivariate logits of the transition probabilities and define, for each page, a content effect and a link effect. The former measures the attractiveness of the page due to its contents, while the latter signals its ability to suggest further interesting links within the site. Moreover, we define an additional effect, representing overall page success, which incorporates both effects previously described. Using WinBUGS, we provide estimates and credible intervals for each of the above effects and rank pages accordingly.     

Applicability of a Neuroprobabilistic Integral Risk Index for the Environmental Management of Polluted Areas: A Case Study

Recently, we developed a GIS-Integrated Integral Risk Index (IRI) to assess human health risks in areas with presence of environmental pollutants. Contaminants were previously ranked by applying a self-organizing map (SOM) to their characteristics of persistence, bioaccumulation, and toxicity in order to obtain the Hazard Index (HI). In the present study, the original IRI was substantially improved by allowing the entrance of probabilistic data. A neuroprobabilistic HI was developed by combining SOM and Monte Carlo analysis. In general terms, the deterministic and probabilistic HIs followed a similar pattern: polychlorinated biphenyls (PCBs) and light polycyclic aromatic hydrocarbons (PAHs) were the pollutants showing the highest and lowest values of HI, respectively. However, the bioaccumulation value of heavy metals notably increased after considering a probability density function to explain the bioaccumulation factor. To check its applicability, a case study was investigated. The probabilistic integral risk was calculated in the chemical/petrochemical industrial area of Tarragona (Catalonia, Spain), where an environmental program has been carried out since 2002. The risk change between 2002 and 2005 was evaluated on the basis of probabilistic data of the levels of various pollutants in soils. The results indicated that the risk of the chemicals under study did not follow a homogeneous tendency. However, the current levels of pollution do not mean a relevant source of health risks for the local population. Moreover, the neuroprobabilistic HI seems to be an adequate tool to be taken into account in risk assessment processes.