Cost effectiveness analysis of laparoscopic hysterectomy compared with standard hysterectomy: results from a randomised trial

Objective: To assess the cost effectiveness of laparoscopic hysterectomy compared with conventional hysterectomy (abdominal or vaginal). Design: Cost effectiveness analysis based on two parallel trials: laparoscopic (n = 324) compared with vaginal hysterectomy (n = 163); and laparoscopic (n = 573) compared with abdominal hysterectomy (n = 286). Participants: 1346 women requiring a hysterectomy for reasons other than malignancy. Main outcome measure: One year costs estimated from NHS perspective. Health outcomes expressed in terms of QALYs based on women's responses to the EQ-5D at baseline and at three points during up to 52 weeks' follow up. Results: Laparoscopic hysterectomy cost an average of pound401 ($708; C571) more (95$471789; E380 437). The, probability that laparoscopic hysterectomy is cost effective was below 50% for a large range of values of willingness to pay for an additional QALY. Laparoscopic hysterectomy cost an average of pound186 ($328; E265) more than abdominal hysterectomy, although 95$46 893; E37 813). If the NHS is willing to pay pound30 0 00 for an additional QALY, the probability that laparoscopic hysterectomy is cost effective is 56%. Conclusions: Laparoscopic hysterectomy is not cost effective relative to vaginal hysterectomy. Its cost effectiveness relative to the abdominal procedure is finely balanced

Balanced electron-hole transport in spin-orbit semimetal SrIrO3 heterostructures

Relating the band structure of correlated semimetals to their transport properties is a complex and often open issue. The partial occupation of numerous electron and hole bands can result in properties that are seemingly in contrast with one another, complicating the extraction of the transport coefficients of different bands. The 5d oxide SrIrO3 hosts parabolic bands of heavy holes and light electrons in gapped Dirac cones due to the interplay between electron-electron interactions and spin-orbit coupling. We present a multifold approach relying on different experimental techniques and theoretical calculations to disentangle its complex electronic properties. By combining magnetotransport and thermoelectric measurements in a field-effect geometry with first-principles calculations, we quantitatively determine the transport coefficients of different conduction channels. Despite their different dispersion relationships, electrons and holes are found to have strikingly similar transport coefficients, yielding a holelike response under field-effect and thermoelectric measurements and a linear, electronlike Hall effect up to 33 T.Comment: 5 pages, 4 figure

Implementation of comparative effectiveness research in personalized medicine applications in oncology: current and future perspectives

Personalized medicine (PM) or precision medicine has been defined as an innovative approach that takes into account individual differences in people's genes, environments, and lifestyles in prevention and treatment of disease. In PM, genomic information may contribute to the molecular understanding of disease, to optimize preventive health care strategies, and to fit the best drug therapies to the patient's individual characteristics. Evidence development in the era of genomic medicine is extremely challenging due to a number of factors. These include the rapid technological innovation in molecular diagnostics and targeted drug discoveries, and hence the large number of mutations and multiple ways these may influence treatment decisions. Although the evidence base for PM is evolving rapidly, the main question to be explored in this article is whether existing evidence is also fit for comparative effectiveness research (CER). As a starting point, this paper therefore reflects on the evidence required for CER and the evidence gaps preventing decisions on market access and coverage. The paper then discusses challenges and potential barriers for applying a CER paradigm to PM, identifies common methodologies for designing clinical trials in PM, discusses various approaches for analyzing clinical trials to infer from population to individual level, and presents an example of a clinical trial in PM (The RxPONDER TRIAL) demonstrating good practice. The paper concludes with a future perspective, including modeling approaches for evidence synthesi

Some new accelerating structures for high current intensity accelerators

The space charge effect inside the beam of charged particles causes serious problems in high-intensity linear particle accelerators. At the present time there is only the Alvarez-Blewett accelerating structure which uses continuous strong focusing to keep the beam confined to the axis. The most serious problem is at the injection end of the accelerator where the particle velocities are low and the drift tube structure has decreased efficiency. It is suggested that some new or less well known accelerating structures using rf quadrupole focusing and H-type mode resonators may solve this problem. The disk-and-washer structure with very high coupling coefficient can be used successfully for the high-energy part of the accelerator. A newly developed coaxial coupler solves the problem of the intertank rf coupling

Deterministic and stochastic P systems for modelling cellular processes

This paper presents two approaches based on metabolic and stochastic P systems, together with their associated analysis methods, for modelling biological sys- tems and illustrates their use through two case studies.Kingdom's Engineering and Physical Sciences Research Council EP/ E017215/1Biotechnology and Biological Sciences Research Council/United Kingdom BB/D019613/1Biotechnology and Biological Sciences Research Council/United Kingdom BB/F01855X/

Decision Curve Analysis for Personalized Treatment Choice between Multiple Options.

BACKGROUND Decision curve analysis can be used to determine whether a personalized model for treatment benefit would lead to better clinical decisions. Decision curve analysis methods have been described to estimate treatment benefit using data from a single randomized controlled trial. OBJECTIVES Our main objective is to extend the decision curve analysis methodology to the scenario in which several treatment options exist and evidence about their effects comes from a set of trials, synthesized using network meta-analysis (NMA). METHODS We describe the steps needed to estimate the net benefit of a prediction model using evidence from studies synthesized in an NMA. We show how to compare personalized versus one-size-fit-all treatment decision-making strategies, such as "treat none" or "treat all patients with a specific treatment" strategies. First, threshold values for each included treatment need to be defined (i.e., the minimum risk difference compared with control that renders a treatment worth taking). The net benefit per strategy can then be plotted for a plausible range of threshold values to reveal the most clinically useful strategy. We applied our methodology to an NMA prediction model for relapsing-remitting multiple sclerosis, which can be used to choose between natalizumab, dimethyl fumarate, glatiramer acetate, and placebo. RESULTS We illustrated the extended decision curve analysis methodology using several threshold value combinations for each available treatment. For the examined threshold values, the "treat patients according to the prediction model" strategy performs either better than or close to the one-size-fit-all treatment strategies. However, even small differences may be important in clinical decision making. As the advantage of the personalized model was not consistent across all thresholds, improving the existing model (by including, for example, predictors that will increase discrimination) is needed before advocating its clinical usefulness. CONCLUSIONS This novel extension of decision curve analysis can be applied to NMA-based prediction models to evaluate their use to aid treatment decision making. HIGHLIGHTS Decision curve analysis is extended into a (network) meta-analysis framework.Personalized models predicting treatment benefit are evaluated when several treatment options are available and evidence about their effects comes from a set of trials.Detailed steps to compare personalized versus one-size-fit-all treatment decision-making strategies are outlined.This extension of decision curve analysis can be applied to (network) meta-analysis-based prediction models to evaluate their use to aid treatment decision making