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Objective: Predicting the impact of any research article on its scientific discipline is often viewed as requiring the passage of time. A recent BMJ article, however, reported that an article's citation rate at 2years could be predicted by data available 3weeks following publication. The question remains as to whether establishing a citation trajectory at an early stage holds for psychiatric publications, given the low percentage of psychiatric articles in their analysis. The aim of the current article was to critically examine this area of the scientific literature. Method: Data were collected from the Institute for Scientific Information on scientific papers published in January/February 2006, in the top 30 psychiatric journals. Analyses examined the comparative impact of early citation numbers and several predictors identified in the BMJ article. Results: Only two BMJ variables (a larger number of references per article and larger number of authors) predicted higher citations at 2years in the principal analysis. Citation counts at 1, 3, 6 and 12months predicted citations at 2years, with increasing success over time, and such citation counts were distinctly superior to the quantified variables in the previous study. Conclusions: It appears doubtful that data available at 3weeks after publication for psychiatric articles are useful in predicting citation counts at 2years. The trajectory of citation counts for a psychiatric article becomes more apparent with time. [ABSTRACT FROM AUTHOR]

Objective: This paper aims to describe and test novel computational driver models, predicting drivers' brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC).Background: Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving.Method: Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers' arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study.Results: The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study.Conclusion: Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data.Application: Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving. [ABSTRACT FROM AUTHOR]

After the diagnosis of a disease, one major objective is to predict cumulative probabilities of events such as clinical relapse or death from the individual information collected up to a prediction time, usually including biomarker repeated measurements. Several competing estimators have been proposed, mainly from two approaches: joint modelling and landmarking. These approaches differ by the information used, the model assumptions and the complexity of the computational procedures. This paper aims to review the two approaches, precisely define the derived estimators of dynamic predictions and compare their performances notably in case of misspecification. The ultimate goal is to provide key elements for the use of individual dynamic predictions in clinical practice. Prediction of two competing causes of prostate cancer progression from the history of prostate-specific antigen is used as a motivated example. We formally define the quantity to estimate and its estimators, propose techniques to assess the uncertainty around predictions and validate them. We then conduct an in-depth simulation study compare the estimators in terms of prediction error, discriminatory power, efficiency and robustness to model assumptions. We show that prediction tools should be handled with care, in particular by properly specifying models and estimators. [ABSTRACT FROM AUTHOR]

Count outcomes with excessive zeros are common in behavioral and social studies, and zero-inflated count models such as zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) can be applied when such zero-inflated count data are used as response variable. However, when the zero-inflated count data are used as predictors, ignoring the difference of structural and random zeros can result in biased estimates. In this paper, a generalized estimating equation (GEE)-type mixture model is proposed to jointly model the response of interest and the zero-inflated count predictors. Simulation studies show that the proposed method performs well for practical settings and is more robust for model misspecification than the likelihood-based approach. A case study is also provided for illustration. [ABSTRACT FROM AUTHOR]

Projections of both the short- and long-term course of the variant Creutzfeldt-Jakob disease (vCJD) epidemic in Great Britain have demonstrated great uncertainty due to the lack of knowledge of key aspects of the biology and epidemiology of this new disease. Such projections are sensitive to assumptions made regarding the pattern of exposure to BSE-infected animals, the effectiveness of control measures introduced in 1989 and 1996 in reducing this exposure, the functional form of the incubation period distribution and patterns of age-dependent susceptibility/exposure. This paper provides short-term projections for vCJD onsets using the time- and age-distributed onset data to the end of 2000, with results that are directly comparable to the other papers in this issue. These results demonstrate the continued uncertainty in the future scale of this disease. [ABSTRACT FROM AUTHOR]

Incubation period of the new variant Creutzfeldt-Jakob disease (vCJD) from infection to clinical onset and the eventual impact of the disease remain major concerns. Based on i) epidemiological conceptualization of human exposure to BSE contaminated material, ii) exponentially decreasing susceptibility after 15 years of age, and iii) typical incubation period (IP) distributions for time from infection to onset, we have previously estimated mean incubation period and projected number of vCJD cases. In this paper, we investigate the robustness of these estimates with respect to i-iii using the UK's 113 vCJD cases with clinical onset before December 2000. Mean incubation period was estimated at 16.4 years (95% CI 11.4-24.8), 15.9 years (95% CI 11.4-22.0), 14.1 years (95% CI 10.4-24.2) with the log-normal, Gamma and Weibull distributions respectively. Corresponding predictions for the total size of the epidemic ranged from 183 to 304. Maximal susceptibility to infection between 1.3 and 15.9 years and decreasing by 15% per year of age thereafter yielded the best fit. The shape of the IP distribution did not affect the predictions. In summary, within a set of reasonable assumptions, mean incubation period for vCJD ranged from 15 to 20 years, and the eventual impact of vCJD was a few hundred patients. [ABSTRACT FROM AUTHOR]