Suchergebnisse

Model averaging is a useful approach for capturing uncertainty due to model selection. Currently, this uncertainty is often quantified by means of approximations that do not easily extend to simultaneous inference. Moreover, in practice there is a need for both model averaging and simultaneous inference for derived parameters calculated in an after‐fitting step. We propose a method for obtaining asymptotically correct standard errors for one or several model‐averaged estimates of derived parameters and for obtaining simultaneous confidence intervals that asymptotically control the family‐wise Type I error rate. The performance of the method in terms of coverage is evaluated using a simulation study and the applicability of the method is demonstrated by means of three concrete examples.

AbstractIn this review, recent methodological developments for the benchmark dose (BMD) methodology are summarized. Specifically, we introduce the advances for the main steps in BMD derivation: selecting the procedure for defining a BMD from a predefined benchmark response (BMR), setting a BMR, selecting a dose–response model, and estimating the corresponding BMD lower limit (BMDL). Although the last decade has shown major progress in the development of BMD methodology, there is still room for improvement. Remaining challenges are the implementation of new statistical methods in user‐friendly software and the lack of consensus about how to derive the BMDL.

AbstractWe propose benchmark dose estimation for event‐time data, using a two‐step approach. This approach avoids estimation of complex models and has been previously shown to give robust results for summarizing relevant parameters for risk assessment. In the first step, the probability of the event of interest to occur (in a certain time interval) is described as a function of time, resulting in an event‐time model; such a model is fitted allowing an individual curve for each dose, and relevant estimates are extracted. In the second step, a dose–response model is fitted to the estimates of t50 obtained from the event‐time model in the first step. Given a predefined benchmark response, the benchmark dose is then estimated from the resulting model. This novel approach is demonstrated in two examples. Our application of the time‐to‐event model showed a gain in power compared to the traditional analysis of end‐of‐study summary data.

Over the recent decades, there has been an increasing focus on energy-efficient operation of vessels. It has become part of the political agenda, where regulation is the main driver, but the maritime industry itself has also been driven towards more energy-efficient operation of the vessels, due to increasing fuel costs. Improving the energy efficiency on board vessels is not only a technical issue - factors such as awareness of the problem, knowledge skills and motivation are also important parameters that must be considered. The paper shows how training in energy-efficient operation and awareness can affect the energy consumption of vessels. The study is based on navigational, full-mission simulator tests conducted at the International Maritime Academy SIMAC. A full-mission simulator is an image of the world allowing the students to obtain skills through learning-by-doing in a safe environment. Human factors and technical issues were included and the test sessions consisted of a combination of practical simulator exercises and reflection workshops. The result of the simulator tests showed that a combination of installing technical equipment and raising awareness - making room for reflections-on and in-action - has a positive effect on energy consumption. The participants, on average, saved approximately 10% in fuel.

Background: Physical comorbidities and substance use are commonly reported in patients with mental disorders. Aim: To examine somatic comorbidity in patients with substance use disorders (SUD) compared to patients with mental disorders but no SUD. Methods: Lifetime prevalence data on mental and physical health status were collected from inpatients in 12 mental health care facilities in five different countries. Differences in somatic comorbidity were examined by means of logistic regression analysis controlling for age and gender. Results: Of 2,338 patients, 447 (19%) had a primary or secondary SUD diagnosis. In comparison to patients with other mental disorders, patients with SUD had a higher prevalence of infectious and digestive diseases but a lower prevalence of endocrine, nutritional and metabolic disorders. Patterns of physical comorbidities differed according to type of substance used (alcohol use – cardiovascular; tobacco use – respiratory, neoplasms; cannabinoid use – injuries; opioid use – infectious, digestive; benzodiazepine use – endocrine, nutritional, metabolic; stimulants – urogenital). Conclusions: SUD are related to specific somatic health risks while some of our findings point to potentially protective effects. The widespread prescription of benzodiazepines requires research on physical health effects. Early detection of SUD and their integration into programmes targeting physical comorbidity should be a priority in organizing mental health care.