Towards cavity enhanced detection of single ions in the solid-state

Entanglement between single spins and photons is an important resource in quantum information science, e.g. in quantum networking. Rare-earth ions in dielectric crystals have emerged as a promising candidate for this task. Rare-earth ion doped solids have been used so far mostly as ensemble-based quantum memories. However, they also feature promising properties for single ion detection and manipulation, such as long-lived spins, narrow optical transitions and preservation of coherence properties when inserted in nanostructures. Because of the long lifetimes of the optical tThe goal of this master thesis was to take initial steps towards cavity-enhanced detection of single rare-earth ions in the solid state. A fibre microcavity set-up for use with Praseodymium-doped nanocrystals was designed, built and characterised with light at 606 nm, with the aim to achieve Purcell enhancement. The cavity has a finesse of 3374, linewidth of 14 GHz and FSR of 80 THz. In addition, fluorescence and linewidth measurements of Er3+:Y2O3 nanocrystals at room temperature were taken in the telecom range. A primary fluorescence peak at 1535.424 nm with FWHM of 171 GHz was observed, corresponding to the Er3+:4I13/2 ? 4I15/2 transition, and a secondary peak at 1536.851 nm. A population decay time T1 of 13.3 ms was measured

Algorithmic parameterization of mixed treatment comparisons

Mixed Treatment Comparisons (MTCs) enable the simultaneous meta-analysis (data pooling) of networks of clinical trials comparing ≥2 alternative treatments. Inconsistency models are critical in MTC to assess the overall consistency between evidence sources. Only in the absence of considerable inconsistency can the results of an MTC (consistency) model be trusted. However, inconsistency model specification is non-trivial when multi-arm trials are present in the evidence structure. In this paper, we define the parameterization problem for inconsistency models in mathematical terms and provide an algorithm for the generation of inconsistency models. We evaluate running-time of the algorithm by generating models for 15 published evidence structures

A Microsoft-Excel-based tool for running and critically appraising network meta-analyses--an overview and application of NetMetaXL.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.BACKGROUND: The use of network meta-analysis has increased dramatically in recent years. WinBUGS, a freely available Bayesian software package, has been the most widely used software package to conduct network meta-analyses. However, the learning curve for WinBUGS can be daunting, especially for new users. Furthermore, critical appraisal of network meta-analyses conducted in WinBUGS can be challenging given its limited data manipulation capabilities and the fact that generation of graphical output from network meta-analyses often relies on different software packages than the analyses themselves. METHODS: We developed a freely available Microsoft-Excel-based tool called NetMetaXL, programmed in Visual Basic for Applications, which provides an interface for conducting a Bayesian network meta-analysis using WinBUGS from within Microsoft Excel. . This tool allows the user to easily prepare and enter data, set model assumptions, and run the network meta-analysis, with results being automatically displayed in an Excel spreadsheet. It also contains macros that use NetMetaXL's interface to generate evidence network diagrams, forest plots, league tables of pairwise comparisons, probability plots (rankograms), and inconsistency plots within Microsoft Excel. All figures generated are publication quality, thereby increasing the efficiency of knowledge transfer and manuscript preparation. RESULTS: We demonstrate the application of NetMetaXL using data from a network meta-analysis published previously which compares combined resynchronization and implantable defibrillator therapy in left ventricular dysfunction. We replicate results from the previous publication while demonstrating result summaries generated by the software. CONCLUSIONS: Use of the freely available NetMetaXL successfully demonstrated its ability to make running network meta-analyses more accessible to novice WinBUGS users by allowing analyses to be conducted entirely within Microsoft Excel. NetMetaXL also allows for more efficient and transparent critical appraisal of network meta-analyses, enhanced standardization of reporting, and integration with health economic evaluations which are frequently Excel-based.CC is a recipient of a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (funding reference number—CGV 121171) and is a trainee on the Canadian Institutes of Health Research Drug Safety and Effectiveness Network team grant (funding reference number—116573). BH is funded by a New Investigator award from the Canadian Institutes of Health Research and the Drug Safety and Effectiveness Network. This research was partly supported by funding from CADTH as part of a project to develop Excel-based tools to support the conduct of health technology assessments. This research was also supported by Cornerstone Research Group

Automated generation of node-splitting models for assessment of inconsistency in network meta-analysis

Network meta-analysis enables the simultaneous synthesis of a network of clinical trials comparing any number of treatments. Potential inconsistencies between estimates of relative treatment effects are an important concern, and several methods to detect inconsistency have been proposed. This paper is concerned with the node-splitting approach, which is particularly attractive because of its straightforward interpretation, contrasting estimates from both direct and indirect evidence. However, node-splitting analyses are labour-intensive because each comparison of interest requires a separate model. It would be advantageous if node-splitting models could be estimated automatically for all comparisons of interest. We present an unambiguous decision rule to choose which comparisons to split, and prove that it selects only comparisons in potentially inconsistent loops in the network, and that all potentially inconsistent loops in the network are investigated. Moreover, the decision rule circumvents problems with the parameterisation of multi-arm trials, ensuring that model generation is trivial in all cases. Thus, our methods eliminate most of the manual work involved in using the node-splitting approach, enabling the analyst to focus on interpreting the results. (C) 2015 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd

Evidence-based prescribing: combining network meta-analysis with multicriteria decision analysis to choose among multiple drugs

What is the drug of choice for condition x? is among the most commonly asked questions in primary care.1 Reflecting the complexity of prescribing decisions, answering this question requires a difficult trade-off between the benefits and harms of multiple drugs for a given condition. The principles of evidence-based medicine suggest that prescribing decisions should be guided by an objective benchmark, namely scientific evidence.2 Such evidence is particularly important when choosing a first-line treatment among multiple alternatives. Unfortunately, existing clinical evidence on benefits and harms is rarely adequate to inform prescribing decisions. A randomized controlled trial comparing all relevant drugs would provide such information. However, clinical trials are often designed for regulatory purposes and, therefore, include selective patient populations and do not include all available comparator drugs.3,4 To obtain insight into the comparative benefits and harms of multiple drugs, prescribers turn to summaries of evidence to discern the most promising drugs from their less effective comparators. Recent methods used to synthesize existing evidence provide much-needed information on the comparative benefits and harms of multiple drugs. Network meta-analysis is one such method that allows for the combination of direct and indirect evidences from randomized trials, facilitating the comparison of all relevant drugs even when they are not directly compared with each other in clinical trials.5 The recent surge in the number of network meta-analyses in the general medical literature is a testament to the increasing need for comparative evidence in prescribing decisions

Applying multiple criteria decision analysis to comparative benefit-risk assessment: choosing among statins in primary prevention

Decision makers in different health care settings need to weigh the benefits and harms of alternative treatment strategies. Such health care decisions include marketing authorization by regulatory agencies, practice guideline formulation by clinical groups, and treatment selection by prescribers and patients in clinical practice. Multiple criteria decision analysis (MCDA) is a family of formal methods that help make explicit the tradeoffs that decision makers accept between the benefit and risk outcomes of different treatment options. Despite the recent interest in MCDA, certain methodological aspects are poorly understood. This paper presents 7 guidelines for applying MCDA in benefitrisk assessment and illustrates their use in the selection of a statin drug for the primary prevention of cardiovascular disease. We provide guidance on the key methodological issues of how to define the decision problem, how to select a set of nonoverlapping evaluation criteria, how to synthesize and summarize the evidence, how to translate relative measures to absolute ones that permit comparisons between the criteria, how to define suitable scale ranges, how to elicit partial preference information from the decision makers, and how to incorporate uncertainty in the analysis. Our example on statins indicates that fluvastatin is likely to be the most preferred drug by our decision maker and that this result is insensitive to the amount of preference information incorporated in the analysis

A multi-criteria decision analysis perspective on the health economic evaluation of medical interventions

Abstract A standard practice in health economic evaluation is to monetize health effects by assuming a certain societal willingness-to-pay per unit of health gain. Although the resulting net monetary benefit (NMB) is easy to compute, the use of a single willingness-to-pay threshold assumes expressibility of the health effects on a single nonmonetary scale. To relax this assumption, this article proves that the NMB framework is a special case of the more general stochastic multi-criteria acceptability analysis (SMAA) method. Specifically, as SMAA does not restrict the number of criteria to two and also does not require the marginal rates of substitution to be constant, there are problem instances for which the use of this more general method may result in a better understanding of the tradeoffs underlying the reimbursement decision-making problem. This is illustrated by applying both methods in a case study related to infertility treatment