Bayesian value-of-infomation analysis: an application to a policy model of Alzheimer's disease

A framework is presented that distinguishes the conceptually separate decisions of which treatment strategy is optimal from the question of whether more information is required to inform this choice in the future. The authors argue that the choice of treatment strategy should be based on expected utility, and the only valid reason to characterize the uncertainty surrounding outcomes of interest is to establish the value of acquiring additional information. A Bayesian decision theoretic approach is demonstrated through a probabilistic analysis of a published policy model of Alzheimer’s disease. The expected value of perfect information is estimated for the decision to adopt a new pharmaceutical for the population of patients with Alzheimer’s disease in the United States. This provides an upper bound on the value of additional research. The value of information is also estimated for each of the model inputs. This analysis can focus future research by identifying those parameters where more precise estimates would be most valuable and indicating whether an experimental design would be required. We also discuss how this type of analysis can also be used to design experimental research efficiently (identifying optimal sample size and optimal sample allocation) based on the marginal cost and marginal benefit of sample information. Value-of-information analysis can provide a measure of the expected payoff from proposed research, which can be used to set priorities in research and development. It can also inform an efficient regulatory framework for new healthcare technologies: an analysis of the value of information would define when a claim for a new technology should be deemed substantiated and when evidence should be considered competent and reliable when it is not cost-effective to gather any more information

Tverberg-type theorems for intersecting by rays

In this paper we consider some results on intersection between rays and a given family of convex, compact sets. These results are similar to the center point theorem, and Tverberg's theorem on partitions of a point set

Vacancy-assisted domain-growth in asymmetric binary alloys: a Monte Carlo study

A Monte Carlo simulation study of the vacancy-assisted domain-growth in asymmetric binary alloys is presented. The system is modeled using a three-state ABV Hamiltonian which includes an asymmetry term, not considered in previous works. Our simulated system is a stoichiometric two-dimensional binary alloy with a single vacancy which evolves according to the vacancy-atom exchange mechanism. We obtain that, compared to the symmetric case, the ordering process slows down dramatically. Concerning the asymptotic behavior it is algebraic and characterized by the Allen-Cahn growth exponent x=1/2. The late stages of the evolution are preceded by a transient regime strongly affected by both the temperature and the degree of asymmetry of the alloy. The results are discussed and compared to those obtained for the symmetric case.Comment: 21 pages, 9 figures, accepted for publication in Phys. Rev.

advanced electric propulsion diagnostic tools at iom

Abstract Recently, we have set up an Advanced Electric Propulsion Diagnostic (AEPD) platform [1] , which allows for the in-situ measurement of a comprehensive set of thruster performance parameters. The platform utilizes a five-axis-movement system for precise positioning of the thruster with respect to the diagnostic heads. In the first setup (AEPD1) an energy-selective mass spectrometer (ESMS) and a miniaturized Faraday probe for ion beam characterization, a telemicroscope and a triangular laser head for measuring the erosion of mechanical parts, and a pyrometer for surface temperature measurements were integrated. The capabilities of the AEPD1 platform were demonstrated with two electric propulsion thrusters, a gridded ion thruster RIT 22 (Airbus Defence & Space, Germany, [13]) and a Hall effect thruster SPT 100D EM1 (EDB Fakel, Russia, [1] , [4] ), in two different vacuum facilities

Ferroelectric Thin-Film Capacitors and Piezoelectric Switches for Mobile Communication Applications

Thin-film ferroelectric capacitors have been integrated with resistors and active functions such as ESD protection into small, miniaturized modules, which enable a board space saving of up to 80%. With the optimum materials and processes, integrated capacitors with capacitance densities of up to 100 nF/mm2 for stacked capacitors combined with breakdown voltages of 90 V have been achieved. The integration of these high-density capacitors with extremely high breakdown voltage is a major accomplishment in the world of passive components and has not yet been reported for any other passive integration technology. Furthermore, thin-film tunable capacitors based on barium strontium titanate with high tuning range and high quality factor at 1 GHz have been demonstrated. Finally, piezoelectric thin films for piezoelectric switches with high switching speed have been realized

Is the eye a window to the brain in Sanfilippo syndrome?

Published online: 17 November 2020Sanfilippo syndrome is an untreatable form of childhood-onset dementia. Whilst several therapeutic strategies are being evaluated in human clinical trials including i.v. delivery of AAV9-based gene therapy, an urgent unmet need is the availability of non-invasive, quantitative measures of neurodegeneration. We hypothesise that as part of the central nervous system, the retina may provide a window through which to 'visualise' degenerative lesions in brain and amelioration of them following treatment. This is reliant on the age of onset and the rate of disease progression being equivalent in retina and brain. For the first time we have assessed in parallel, the nature, age of onset and rate of retinal and brain degeneration in a mouse model of Sanfilippo syndrome. Significant accumulation of heparan sulphate and expansion of the endo/lysosomal system was observed in both retina and brain pre-symptomatically (by 3 weeks of age). Robust and early activation of micro- and macroglia was also observed in both tissues. There was substantial thinning of retina and loss of rod and cone photoreceptors by ~ 12 weeks of age, a time at which cognitive symptoms are noted. Intravenous delivery of a clinically relevant AAV9-human sulphamidase vector to neonatal mice prevented disease lesion appearance in retina and most areas of brain when assessed 6 weeks later. Collectively, the findings highlight the previously unrecognised early and significant involvement of retina in the Sanfilippo disease process, lesions that are preventable by neonatal treatment with AAV9-sulphamidase. Critically, our data demonstrate for the first time that the advancement of retinal disease parallels that occurring in brain in Sanfilippo syndrome, thus retina may provide an easily accessible neural tissue via which brain disease development and its amelioration with treatment can be monitored.Helen Beard, Glyn Chidlow, Daniel Neumann, Nazzmer Nazri, Meghan Douglass, Paul J. Trim, Marten F. Snel, Robert J. Casson and Kim M. Hemsle

Towards Machine Wald

The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed \emph{by humans} because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to \emph{think} as \emph{humans} have the ability to do when faced with uncertainty is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tend to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with Decision Theory, Machine Learning, Bayesian Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty Quantification and Information Based Complexity.Comment: 37 page