Evacetrapib and Cardiovascular Outcomes in High-Risk Vascular Disease

BACKGROUND: The cholesteryl ester transfer protein inhibitor evacetrapib substantially raises the high-density lipoprotein (HDL) cholesterol level, reduces the low-density lipoprotein (LDL) cholesterol level, and enhances cellular cholesterol efflux capacity. We sought to determine the effect of evacetrapib on major adverse cardiovascular outcomes in patients with high-risk vascular disease. METHODS: In a multicenter, randomized, double-blind, placebo-controlled phase 3 trial, we enrolled 12,092 patients who had at least one of the following conditions: an acute coronary syndrome within the previous 30 to 365 days, cerebrovascular atherosclerotic disease, peripheral vascular arterial disease, or diabetes mellitus with coronary artery disease. Patients were randomly assigned to receive either evacetrapib at a dose of 130 mg or matching placebo, administered daily, in addition to standard medical therapy. The primary efficacy end point was the first occurrence of any component of the composite of death from cardiovascular causes, myocardial infarction, stroke, coronary revascularization, or hospitalization for unstable angina. RESULTS: At 3 months, a 31.1% decrease in the mean LDL cholesterol level was observed with evacetrapib versus a 6.0% increase with placebo, and a 133.2% increase in the mean HDL cholesterol level was seen with evacetrapib versus a 1.6% increase with placebo. After 1363 of the planned 1670 primary end-point events had occurred, the data and safety monitoring board recommended that the trial be terminated early because of a lack of efficacy. After a median of 26 months of evacetrapib or placebo, a primary end-point event occurred in 12.9% of the patients in the evacetrapib group and in 12.8% of those in the placebo group (hazard ratio, 1.01; 95% confidence interval, 0.91 to 1.11; P=0.91). CONCLUSIONS: Although the cholesteryl ester transfer protein inhibitor evacetrapib had favorable effects on established lipid biomarkers, treatment with evacetrapib did not result in a lower rate of cardiovascular events than placebo among patients with high-risk vascular disease. (Funded by Eli Lilly; ACCELERATE ClinicalTrials.gov number, NCT01687998 .)

Analysis of the effect of uncertain clamping stiffness on the dynamical

In uncertainty calculation, the inability of interval parameters to take into account mutual dependency is a major shortcoming. When parameters with a geometric perspective are involved, the construction of a model using intervals at discrete locations not only increases the problem dimensionality unnecessarily, but it also assumes no dependency whatsoever, including unrealistic parameter combinations leading to results that probably overestimate the true uncertainty. The concept of modelling uncertainty with a geometric aspect using interval fields eliminates this problem by defining basis functions and expressing the uncertain process as a weighted sum of these functions. The definition of the functions enables the model to take into account geometrically dependent parameters,whereas the coefficients in a non-interactive interval format represent the uncertainty. This paper introduces a new type of interval field specifically tailored for geometrically oriented uncertain parameters, based on a maximum gradient condition to model the dependency. This field definition is then applied to a model of a clamped plate with uncertain clamping stiffness with the purpose of identifying the effects of spatial variability and mean value separately.status: publishe

Bayesian estimation of interval bounds based on limited data

© Proceedings of ISMA 2018 - International Conference on Noise and Vibration Engineering and USD 2018 - International Conference on Uncertainty in Structural Dynamics. All rights reserved. When conducting uncertainty quantification, probability methods are widely used to represent quantities of a variable nature. First, a type of probability density function has to be chosen, often based on initial assumptions and physical boundary conditions, defining a set of stochastically parameters. Because the available data is physically limited to a finite number, there will always be uncertainty on the exact value of these parameters. At some point, the amount of experiments will be too low for the estimated parameters to be of practical use. In the case of very low data availability, one can attempt to use intervals instead to quantify the variability. Because no probability density function is defined, intervals can be used in the presence of low data availability, leaving only an upper and lower bound to be defined. This paper will present a method to estimate interval bounds based on a limited set of experiments, trying to optimally use the information they contain and determine practically usable interval bounds.status: publishe

Derivation of an input interval field decomposition based on expert knowledge using locally defined basis functions

In uncertainty calculation, the inability of interval parameters to take into account mutual dependence is a major shortcoming. When parameters with a geometric perspective are involved, the construction of a model using intervals at discrete locations not only increases the problem dimensionality unnecessarily, but it also assumes no dependency whatsoever, including unrealistic parameter combinations leading to possibly very conservative results. The concept of modelling uncertainty with a geometric aspect using interval fields eliminates this problem by defining basis functions and expressing the uncertain process as a weighted sum of these functions. The definition of the functions enables the model to take into account geometrically dependent parameters, whereas the coefficients in a non-interactive interval format represent the uncertainty. This paper introduces a new type of interval field specifically tailored for geometrically oriented uncertain parameters. The field has a non-interactive interval parameter in each node of the FE mesh to keep the true dimensionality of the uncertainty intact, but it obeys a bound on the gradient of the field to account for the dependency within the field.status: publishe

Estimating uncertain regions on small multidimensional datasets using generalized PDF shapes and polynomial chaos expansion

In uncertainty analysis, estimating the degree of uncertainty based on some physical experiments is an essential part of the process to create robust products. Both at the input and the output side of an available model, experiments may be done, which can then be (inverserely) propagated to obtain uncertain results on the other side. In probabilistic analysis, PDF shape, stochastic moments and correlation may be inferred from this data. In possibilistic analysis, these quantities are hard to interpret physically and are therefore difficult to compute. Instead, interval bounds and dependency information can be determined. This paper presents a strategy to infer both interval bounds and dependency information from a (limited) set of data points in a multidimensional space, based on Polynomial Chaos Expansion and a generalized Probability Density Distribution (PDF) shape.status: accepte

Application of Interval Fields to fit experimental data on deepdrawn components

The possibilistic concept of interval fields has been established as a way to model the occurence of spatially uncertain data. Especially in the case of low data availability, interval fields can be seen as an alternative to the random field concept, its probabilistic counterpart, which generally suffers from convergence problems when little information on the uncertainty is available. In this paper, an interval field will be proposed to capture the uncertain thickness distribution of a mechanically formed piece. This will be done based on a set of five nominally identical pieces, the thickness of which has been measured in a large number of locations. The purpose of the interval field will be twofold: to capture the inherent dependence of the thickness over the surface of the piece, and to accurately capture the interval of possible thicknesses across the entire piece.status: accepte

Robust uncertainty quantification in structural dynamics under scarse experimental modal data: A Bayesian-interval approach

The accurate prediction of the dynamic behaviour of a complex component or system is often difficult due to uncertainty or scatter on the physical parameters in the underlying numerical models. Over the past years, several non-deterministic techniques have been developed to account for these model inaccuracies, supporting an objective assessment of the effect of these uncertainties on the dynamic behaviour. Still, also these methods require a realistic quantification of the scatter in the uncertain model properties in order to have any predictive value. In practice, this information is typically inferred from experiments. This uncertainty quantification is especially challenging in case only fragmentative or scarce experimental data are available, as is often the case when using modal data sets. This work therefore studies the application of these limited data sets for this purpose, and focuses more specifically on the quantification of interval uncertainty based on limited information on experimentally obtained eigenfrequencies. The interval approach, which is deemed to be the most robust against data insufficiency, typically starts from bounding the data using the extreme values in the limited data set. This intuitive approach, while of course representing the experiments, in general yields highly unconservative interval estimates, as the extreme realisations are typically not present in the limited data set. This work introduces a completely new approach for quantifying the bounds on the dynamic properties under scarce modal data. It is based on considering a complete set of parametrized probability density functions to determine likelihood functions, which can then be used in a Bayesian framework. To illustrate the practical applicability of the proposed techniques, the methodology is applied to the well-known DLR AIRMOD test case where in a first step, the bounds on the experimental eigenfrequencies are estimated. Then, based on a calibrated finite element model of the structure, bounds on the frequency response functions are estimated. It is illustrated that the method allows for a largely objective estimation of conservative interval bounds under scarce data.status: Published onlin

Product reliability optimization under plate sheet forming process variability

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On the comparison of two novel Interval Field formulations for the representation of spatial uncertainty

This paper concerns the comparison of two established interval field methods for the representation of spatially varying non-determinism in an FE model: Inverse Distance Weighting (IDW) interpolation and the Local Interval Field Decomposition (LIFD) method. The comparison is first made from a theoretical point of view, highlighting the advantages of both techniques as compared to each other. Next, both IDW and LIFD are applied to a dynamical model of a U-shaped hollow tube and the resulting uncertain regions at the output side of the model are compared qualitatively. It is shown that both techniques are complementary to each other due to the trade-off in their ability to represent the uncertainty set at the output side of the model and the involved computational cost. Keywords: interval fields, uncertainty, non-deterministic modellingGeen ISBNstatus: publishe

Transient Landing Dynamics Analysis for A Lunar Lander with Random and Interval fields

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