196,645 research outputs found
Moving toward the implementation of precision medicine needs highly discriminatory, validated, inexpensive, and easy-to-use prediction models
No full textAnalysis of Italian Rainfall Data with a Hierarchical Bayesian Space-Time Model
No full textClimate and meteorological data are characterised by many different scales of spatial and temporal variability often in conjunction with non stationarity, anisotropy and quite complicated space-time interactions. Furthermore climate and meteorological studies must be carried out on large amount of data, often coming from different sources, in order to capture long and short term dependencies, large and small scale spatial effects. All this leads to severe computational problems and the need for the development of complex ad hoc models. Furthermore, for this reason, meteorologists are often constrained to apply potentially unrealistic simplifying assumptions in order to adopt standard statistical models. This kind of models, generally, assume spatial data to be temporally independent and spatial structure not varying over time (separable covariance structure) to estimate the spatial correlation structure and it do not consider the temporal dynamic of the process and the temporal correlation as a function of the spatial domain (Royle, 2000). These limitations can severely affect the estimates quality and the efficiency of traditional space-time statistical models and methods. Alternative models are relatively easy to formulate in the traditional LMM or GLMM frameworks, but a lack of understanding of the underlying processes and the “curse of dimensionality” make the implementation of these models challenging (Wikle et al., 2002). The Bayesian framework represents a natural way to analyse spatio-temporal data and it gives the concrete possibility to overcome the afore mentioned limits (Berliner, Levine and Shea, 2000). In particular, the hierarchical Bayesian space-time modelling approach allows to deal with space-time dependence and interactions by modelling all the relevant process component in several stages. Such models become feasible to implement in high dimensions. Several recent example of Bayesian hierarchical models are present in the literature: for an extensive review see Huang et al., 2007, Benerjee, Carlin and Gelfand, 2004, Wikle et al. 2003 and Wikle, 2000. In this paper we consider a hierarchical Bayesian space-time model, proposed in Wikle, Berliner and Cressie, 1998, to treat monthly rainfall data related to the Italian area and collected between January 2003 and December 2006. The choice of such model is strictly related to the own features of the precipitation process. It’s fairly well known that precipitation process involves complicated spatial structure, temporal structure and spatio-temporal interactions and that the interest of meteorologist are properly in the understanding the behaviour of this process features in order to build prediction maps or hydrological balance equations and so on. These considerations combined with the further necessity of working with a large dataset don’t allow the use of standard statistical approaches and can be more effectively treated in the hierarchical Bayesian space-time modelling approach. Indeed the chosen model allows us to provide a mechanism for combining data from very different sources; to incorporate physical knowledge and background science in the model development and in the specifications of priors on model parameters; to provide posterior distributions of quantities of interest which can be used for scientific inference strategy and to work with very large datasets. These advantages are reached by the model specification through the following five hierarchical levels: 1) the measurement process, as the precipitation process plus an error term; 2) the large and small scales features, incorporated as a linear combination of three sources of variation: time, space and space-time interaction; 3) model parameters: each of these sources are then represented according to physical knowledge; 4 and 5) priors on parameters and hyperpriors are specified respectively in the fourth and fifth stage to complete the model specification. In Particular in the second stage one can decompose the precipitation process into three meaningful components letting the meteorologist to be able to understand and measure how the rainfall is determined by the spatial effect, by the temporal seasonality and by the space-time interactions too. In this stage, the pure spatial and temporal effect describe the well known climate effect whereas the dynamical short time and small spatial scale effect can be easily interpreted as the weather contribution. In this way the rainfall amount in a given site depends on its spatial location and on which period it has been observed as a consequence of the climate effect but it surely depends also on what had happened in the neighbouring sites and previously in time, in other words on the weather contribution. The estimation of such flexible model is obtain through a complex and computer intensive MCMC procedure. Moreover many of the advances in hierarchical Bayesian spatio-temporal modelling have been properly due to the application of the recent MCMC techniques to the Bayesian theory (Wikle et al., 2002). The aim of the present work is to estimate and to understand the spatial and the temporal large scale features (climate effect) of the precipitation process and to isolate them from the spatio-temporal ones (weather effect) for the Italian area. The obtained information are, in a further step, used to obtain predictions maps. The computations are developed by the authors using the R software environment (Development Core Team, 2007)
A Hierarchical Bayesian Space-Time Model to Analyse the Spatio-Temporal Distribution of the Precipitation in the Italian Area
No full textCirculating adiponectin levels are paradoxically associated with mortality rate. A systematic review and meta-analysis
No full textCONTEXT:
Some studies have surprisingly indicated that serum adiponectin is positively related to mortality rate, thus casting doubts on its role as a therapeutic target for cardiovascular disease.
OBJECTIVE:
To summarize evidence about direction, strength and modulators of this controversial association.
DATA SOURCES:
MEDLINE, Web of Science, CINHAL, Cochrane Library and Scopus from inception through June 2018.
STUDY SELECTION:
English-language prospective studies reporting the association between adiponectin and all-cause or cardiovascular mortality.
DATA EXTRACTION:
Two investigators independently extracted data and assessed study quality using standard criteria following the Preferred Reporting Items for Systematic Reviews and Meta-analyses and The Newcastle-Ottawa Scale, respectively. Pooled hazard ratios (HRs) (95% confidence intervals-CIs) were derived using a fixed or random effects models when appropriated and were expressed for one standard deviation (SD) increment of adiponectin.
DATA SYNTHESIS:
We identified fifty-five (n=61,676 subjects) and twenty-eight (n=43,979 subjects) studies for all-cause and cardiovascular mortality, respectively. Pooled HRs, were 1.24 (1.17-1.31) and 1.28 (1.19-1.37) for all-cause and cardiovascular mortality, respectively. Similar results were obtained also for High Molecular Weight adiponectin. When meta-analyses were restricted to studies reporting data on natriuretic peptides a 43% and 28% reduction on a log scale of these associations were observed after natriuretic peptides adjustment.
CONCLUSIONS:
Our results strongly points to a paradoxical association between high adiponectin levels and increased mortality rate, which is partly modulated by natriuretic peptides
Letter by Menzaghi et al regarding article, "plasma levels of fatty acid-binding protein 4, retinol-binding protein 4, high-molecular-weight adiponectin, and cardiovascular mortality among men with type 2 diabetes: A 22-year prospective study"
No full textValidation of a Modified-Multidimensional Prognostic Index (m-MPI) Including the Mini Nutritional Assessment Short-Form (MNA-SF) for the Prediction of One-Year Mortality in Hospitalized Elderly Patients.
No full textBACKGROUND:
The mortality prediction represents a key factor in the managing of elderly hospitalized patients. Since in older subjects mortality results from a combination of biological, functional, nutritional, psychological and environmental factors, a Multidimensional Prognostic Index (MPI) that predict short- and long-term mortality based on a standardized comprehensive geriatric assessment (CGA) has recently been developed and validated.
OBJECTIVE:
This study compares the accuracy in predicting the mortality of the MPI with a modified version of the MPI (m-MPI) that included the Mini Nutritional Assessment-Short Form (MNA-SF) instead of the standard MNA.
DESIGN:
This prospective study with a one-year follow-up included 4088 hospitalized patients aged 65 years and older. A standardized CGA that included information on functional (Activities of Daily Living, ADL and Instrumental-ADL), cognitive (Short Portable Mental Status Questionnaire), risk of pressure sore (Exton-Smith Scale), comorbidities (CIRS Index), medications, living status and nutritional status (MNA and MNA-SF) was used to calculate the MPI using a previously validated algorithm.
RESULTS:
Higher MPI values were significantly associated with higher mortality rates with a close agreement between the estimated and the observed mortality both after 1-month (MPI1=2.8% versus m-MPI1=2.8%,p=0.946; MPI2=8.9% versus m-MPI2=9%,p=0.904; MPI3=21.9% versus m-MPI3=21.9,p=0.978) and 1-year of follow-up (MPI1=10.8% versus m-MPI1=10.5%,p=0.686; MPI2=27.3% versus m-MPI2=28%, p=0.495; MPI3=52.8% versus m-MPI3=52.7%,p=0.945). The estimated areas under the receiver operating characteristics (ROC) curves suggested a clinically negligible difference between the two indices.
CONCLUSION:
The m-MPI is as sensitive as the MPI in stratifying hospitalized elderly patients into groups at varying risk of short- and long-term mortality, but with fewer items
A dynamic thermoviscoelastic contact problem with the second sound effect
No full textThis paper deals with a contact problem describing the mechanical and thermal
evolution of a damped extensible thermoviscoelastic beam under the Cattaneo law,
relating the heat flux to the gradient of the temperature. The beam is rigidly
clamped at its left end whereas the right end of the beam moves vertically between
reactive stops like a nonlinear spring. Existence and uniqueness of the solution is
proved, as well as the exponential decay of the related energy. Then, fully discrete
approximations are introduced by using the classical finite element method and the
implicit Euler scheme to approximate the spatial variable and to discretize the time
derivatives, respectively. An a priori error estimates result is proved, from which the
linear convergence of the algorithm is deduced. The case where the two stops are
rigid is also studied from the point of view of the existence and longtime behavior
of the solutions. Finally, some numerical simulations are presented to demonstrate
the accuracy of the approximation and the behavior of the solutio
- …
