Maximum Level Of Information Research Articles

Cost-Effective Use of the Protein S Algorithm in Thrombophilia Testing.

One of the most complex risk factors for the laboratory assessment of thrombophilia is Protein S (PS). The testing algorithm for PS employs the plasma-based assays of free PS antigen, total PS antigen, and PS activity creating a complex diagnostic scheme that can lead to misdiagnosis if incorrectly used, and a potential waste of resources and money. This paper compares the recently published evidence-based algorithm from the International Society for Hemostasis and Thrombosis (ISTH) with several commonly performed nonevidence-based testing schemes, to demonstrate the efficiency of the evidence-based algorithm for diagnostic efficiency with improved patient care and increased cost savings for the laboratory. Significant savings (31%-60%) can be realized when the evidence-based algorithm is used in place of other testing modalities of initial PS activity testing (31%) or testing with all 3 assays simultaneously (60%). This study utilizing the PS testing evidence-based algorithm as part of a thrombophilia evaluation demonstrates that the appropriate testing methods can be used to limit wasteful practices while achieving the maximum level of information in this time of limited resources and need for increase monetary savings.

English

Organization and person have a theoretical and ontological link; being the first teleological extension of the latter, it is impossible to think of one without the other. In a similar way, the complex of economy itself cannot overlook the needs of the person and seek for the reach of maximization results with unhesitating self-interest. In this paper, it is argued that every organization is born to satisfy people’s, or stakeholders’ interests, so that a Rational Management must evaluate both the financial-economic results and the social impact of its activity, in order to achieve the maximum level of information in the decision making. Nonetheless, this approach needs a dramatic shift of conceiving the economy and the role of the organization, which must bring back the person (human being) at the center of any theoretical model, lest the creation of unfairness and inequalities. The Rational Management Theory proposed serves this scope, by implementing a rational decision making based on all the aspects of the outcome generated by the organization. This approach is relevant to the current literature, because it shifts the neoclassical economic conception of the activities to one more human-sized and conscious, and applies indifferently to any kind of organization, whether it is public, private or non-profit. Key words: Rational management, business, social impact, management theory, evaluation, organization, person.

A Bayesian fusion model for space-time reconstruction of finely resolved velocities in turbulent flows from low resolution measurements

.The study of turbulent flows calls for measurements with high resolution in both space and time. We propose a new approach to reconstruct high-temporal–high-spatial resolution velocity fields by combining two sources of information that are well resolved either in space or in time, the low-temporal–high-spatial (LTHS) and the high-temporal–low-spatial (HTLS) resolution measurements. In the framework of co-conception between sensing and data post-processing, this work extensively investigates a Bayesian reconstruction approach using a simulated database. A Bayesian fusion model is developed to solve the inverse problem of data reconstruction. The model uses a maximum a posteriori estimate, which yields the most probable field knowing the measurements. The direct numerical simulation (DNS) of a wall-bounded turbulent flow at moderate Reynolds number is used to validate and assess the performances of the present approach. Low-resolution measurements are subsampled in time and space from the fully resolved data. Reconstructed velocities are compared to the reference DNS to estimate the reconstruction errors. The model is compared to other conventional methods such as linear stochastic estimation and cubic spline interpolation. Results show the superior accuracy of the proposed method in all configurations. Further investigations of model performances on various scales demonstrate its robustness. Numerical experiments also permit one to estimate the expected maximum information level corresponding to limitations of experimental instruments.

Increasing the number of discrete character states for continuous characters generates well‐resolved trees that do not reflect phylogeny

Since the introduction of the cladistic method in systematics, continuous characters have been integrated into analyses but no methods for their treatment have received unanimous support. Some methods require a large number of character states to discretise continuous characters in order to keep the maximum level of information about taxa differences within the coding scheme. Our objective was to assess the impact of increasing the character state number on the outcomes of phylogenetic analyses. Analysis of a variety of simulated datasets shows that these methods for coding continuous characters can lead to the generation of well-resolved trees that do not reflect a phylogenetic signal. We call this phenomenon the flattening of the tree-length distribution; it is influenced by both the relative quantity of continuous characters in relation to discrete characters, and the number of characters in relation to the number of taxa. Bootstrap tests provide a method to avoid this potential bias.

Cosmic shear tomography and efficient data compression using COSEBIs

Context. Gravitational lensing is one of the leading tools in understanding the dark side of the Universe. The need for accurate, efficient and effective methods which are able to extract this information along with other cosmological parameters from cosmic shear data is ever growing. COSEBIs, Complete Orthogonal Sets of E-/B-Integrals, is a recently developed statistical measure that encompasses the complete E-/B-mode separable information contained in the shear correlation functions measured on a finite angular range. Aims. The aim of the present work is to test the properties of this newly developed statistics for a higher-dimensional parameter space and to generalize and test it for shear tomography. Methods. We use Fisher analysis to study the effectiveness of COSEBIs. We show our results in terms of figure-of-merit quantities, based on Fisher matrices. Results. We find that a relatively small number of COSEBIs modes is always enough to saturate to the maximum information level. This number is always smaller for 'logarithmic COSEBIs' than for 'linear COSEBIs', and also depends on the number of redshift bins, the number and choice of cosmological parameters, as well as the survey characteristics. Conclusions. COSEBIs provide a very compact way of analyzing cosmic shear data, i.e., all the E-/B-mode separable second-order statistical information in the data is reduced to a small number of COSEBIs modes. Furthermore, with this method the arbitrariness in data binning is no longer an issue since the COSEBIs modes are discrete. Finally, the small number of modes also implies that covariances, and their inverse, are much more conveniently obtainable, e.g., from numerical simulations, than for the shear correlation functions themselves.

On Adaptive Error Spending Approach for Group Sequential Trials with Random Information Levels

A group sequential analysis following the error spending approach of Lan and DeMets (1983) requires that the maximum information level be fixed in advance. In practice, however, the maximum information level is often random, making it impossible to determine the information fractions required by Lan and DeMets (1983) to calculate the sequential boundary. We propose an adaptive error spending approach that further expands practical applications to settings where the interim information levels can depend on blinded accumulating data. We use a simple weighting method to combine independent test statistics from different stages, which are then compared with adaptive boundary values for the group sequential test. We develop a measure-theoretic framework and show that the adaptive error spending approach controls the type 1 error rates. Methods for point estimates and confidence intervals are also proposed. We warn that an error spending approach can lead to serious inflation of the type 1 error rates when the number or timing of interim analyses is allowed to depend on unblinded accumulating data.

A power study of a sequential method of p-value adjustment for correlated continuous endpoints

In the course of clinical (or preclinical) trial studies, it is a common practice to conduct a relatively large number of tests to extract the maximum level of information from the study. It has been known that as the number of tests (or endpoints) increases, the probability of falsely rejecting at least one hypothesis also increases. Single-step methods such as the Bonferroni, Šidá, or James (1) approximation procedure have been used to adjust the p-values for each hypothesis. To reduce the conservatism (i.e., underestimating type I error) possessed by the aforementioned methods Holm (2) proposed a so-called “free-step-down” procedure. This adjustment can be made even less conservative by incorporating the dependence structure of endpoints at each adjustment step of the procedure. That is done by sequentially applying James's approximation procedure for correlated endpoints at each step referred to as the Free-James method. This article primarily compares the power of the Free-James method to the power of the Bonferroni and James single-step-down and the Holm free-step-down methods. Two definitions of power are considered: (a) the probability of correctly rejecting at least one hypothesis when it is true and (b) the probability of correctly rejecting all hypotheses that are true. Monte Carlo simulations show that the Free-James method is as good as other methods under definition (a) and the most powerful under definition (b) for various sample sizes, numbers of endpoints and correlations.