Modelling the cost effectiveness of interferon beta and glatiramer acetate in the management of multiple sclerosis

OBJECTIVE: To evaluate the cost effectiveness of four disease modifying treatments (interferon betas and glatiramer acetate) for relapsing remitting and secondary progressive multiple sclerosis in the United Kingdom. DESIGN: Modelling cost effectiveness. SETTING: UK NHS. PARTICIPANTS: Patients with relapsing remitting multiple sclerosis and secondary progressive multiple sclerosis. MAIN OUTCOME MEASURES: Cost per quality adjusted life year gained. RESULTS: The base case cost per quality adjusted life year gained by using any of the four treatments ranged from £42 000 ($66 469; 61 630) to £98 000 based on efficacy information in the public domain. Uncertainty analysis suggests that the probability of any of these treatments having a cost effectiveness better than £20 000 at 20 years is below 20%. The key determinants of cost effectiveness were the time horizon, the progression of patients after stopping treatment, differential discount rates, and the price of the treatments. CONCLUSIONS: Cost effectiveness varied markedly between the interventions. Uncertainty around point estimates was substantial. This uncertainty could be reduced by conducting research on the true magnitude of the effect of these drugs, the progression of patients after stopping treatment, the costs of care, and the quality of life of the patients. Price was the key modifiable determinant of the cost effectiveness of these treatments

Estimating population cardinal health state valuation models from individual ordinal (rank) health state preference data

Ranking exercises have routinely been used as warm-up exercises within health state valuation surveys. Very little use has been made of the information obtained in this process. Instead, research has focussed upon the analysis of health state valuation data obtained using the visual analogue scale, standard gamble and time trade off methods. Thurstone’s law of comparative judgement postulates a stable relationship between ordinal and cardinal preferences, based upon the information provided by pairwise choices. McFadden proposed that this relationship could be modelled by estimating conditional logistic regression models where alternatives had been ranked. In this paper we report the estimation of such models for the Health Utilities Index Mark 2 and the SF-6D. The results are compared to the conventional regression models estimated from standard gamble data, and to the observed mean standard gamble health state valuations. For both the HUI2 and the SF-6D, the models estimated using rank data are broadly comparable to the models estimated on standard gamble data and the predictive performance of these models is close to that of the standard gamble models. Our research indicates that rank data has the potential to provide useful insights into community health state preferences. However, important questions remain

Estimating population cardinal health state valuation models from individual ordinal (rank) health state preference data

Ranking exercises have routinely been used as warm-up exercises within health state valuation surveys. Very little use has been made of the information obtained in this process. Instead, research has focussed upon the analysis of health state valuation data obtained using the visual analogue scale, standard gamble and time trade off methods. Thurstone’s law of comparative judgement postulates a stable relationship between ordinal and cardinal preferences, based upon the information provided by pairwise choices. McFadden proposed that this relationship could be modelled by estimating conditional logistic regression models where alternatives had been ranked. In this paper we report the estimation of such models for the Health Utilities Index Mark 2 and the SF-6D. The results are compared to the conventional regression models estimated from standard gamble data, and to the observed mean standard gamble health state valuations. For both the HUI2 and the SF-6D, the models estimated using rank data are broadly comparable to the models estimated on standard gamble data and the predictive performance of these models is close to that of the standard gamble models. Our research indicates that rank data has the potential to provide useful insights into community health state preferences. However, important questions remain.health state valuation; HUI-2; SF-6D

Estimating population cardinal health state valuation models from individual ordinal (rank) health state preference data

Ranking exercises have routinely been used as warm-up exercises within health state valuation surveys. Very little use has been made of the information obtained in this process. Instead, research has focussed upon the analysis of health state valuation data obtained using the visual analogue scale, standard gamble and time trade off methods. Thurstone’s law of comparative judgement postulates a stable relationship between ordinal and cardinal preferences, based upon the information provided by pairwise choices. McFadden proposed that this relationship could be modelled by estimating conditional logistic regression models where alternatives had been ranked. In this paper we report the estimation of such models for the Health Utilities Index Mark 2 and the SF-6D. The results are compared to the conventional regression models estimated from standard gamble data, and to the observed mean standard gamble health state valuations. For both the HUI2 and the SF-6D, the models estimated using rank data are broadly comparable to the models estimated on standard gamble data and the predictive performance of these models is close to that of the standard gamble models. Our research indicates that rank data has the potential to provide useful insights into community health state preferences. However, important questions remain

Crisis Translation Training Challenges Arising from New Contexts of Translation

Focused on material design and self-reflective practices, this article discusses a Crisis Translation Training pitched at master-level translation and interpreting students, developed within the research activities carried out for the INTERACT International Crisis Translation Network. The course was designed to enable them to develop a broader skillset in support of multilingual crisis settings. The learning objectives underpinning the materials address training lacunae in enabling linguists to be involved in relief operations (Federici, 2016; O’Brien, 2016). The authors perceive the complementary skills as crucial in the development of language mediation services assisting linguists operating in such zones of liminality as are crisis settings. Multilingual communication in crisis includes professional forms of translation, signing, and interpreting, as well as forms of intercultural mediation, and social work (Drugan, 2017). Emergencies and prolonged crises have an impact on the communicative dynamics among international relief operators, local institutions, and crisis-affected populations. The authors developed training materials to prepare students to work in crisis settings by harnessing their language competences in crisis translation as a form of community translation (Taibi and Ozolins, 2016). Culturally and linguistically diverse (CALD) communities often need support in language combinations that rarely match commercially viable combinations (Federici and Cadwell, 2018; Shackleton, 2018). This article critically reviews non-language specific Crisis Translation Training, delivered in three iterations across two sites. Reporting on the first phases of the process of material design and enhancement, the article reflects on how issues in delivery, emerging findings regarding the authentic needs of mostly untrained translators, and different pathways of delivery shaped the re-definition of the initial learning objectives and pushed towards a translator trainer approach that would suit a range of new contexts of language mediation

Double Strand Breaks Can Initiate Gene Silencing and SIRT1-Dependent Onset of DNA Methylation in an Exogenous Promoter CpG Island

Chronic exposure to inducers of DNA base oxidation and single and double strand breaks contribute to tumorigenesis. In addition to the genetic changes caused by this DNA damage, such tumors often contain epigenetically silenced genes with aberrant promoter region CpG island DNA hypermethylation. We herein explore the relationships between such DNA damage and epigenetic gene silencing using an experimental model in which we induce a defined double strand break in an exogenous promoter construct of the E-cadherin CpG island, which is frequently aberrantly DNA hypermethylated in epithelial cancers. Following the onset of repair of the break, we observe recruitment to the site of damage of key proteins involved in establishing and maintaining transcriptional repression, namely SIRT1, EZH2, DNMT1, and DNMT3B, and the appearance of the silencing histone modifications, hypoacetyl H4K16, H3K9me2 and me3, and H3K27me3. Although in most cells selected after the break, DNA repair occurs faithfully with preservation of activity of the promoter, a small percentage of the plated cells demonstrate induction of heritable silencing. The chromatin around the break site in such a silent clone is enriched for most of the above silent chromatin proteins and histone marks, and the region harbors the appearance of increasing DNA methylation in the CpG island of the promoter. During the acute break, SIRT1 appears to be required for the transient recruitment of DNMT3B and subsequent methylation of the promoter in the silent clones. Taken together, our data suggest that normal repair of a DNA break can occasionally cause heritable silencing of a CpG island–containing promoter by recruitment of proteins involved in silencing. Furthermore, with contribution of the stress-related protein SIRT1, the break can lead to the onset of aberrant CpG island DNA methylation, which is frequently associated with tight gene silencing in cancer

Evaluation of elicitation methods to quantify Bayes linear models

The Bayes linear methodology allows decision makers to express their subjective beliefs and adjust these beliefs as observations are made. It is similar in spirit to probabilistic Bayesian approaches, but differs as it uses expectation as its primitive. While substantial work has been carried out in Bayes linear analysis, both in terms of theory development and application, there is little published material on the elicitation of structured expert judgement to quantify models. This paper investigates different methods that could be used by analysts when creating an elicitation process. The theoretical underpinnings of the elicitation methods developed are explored and an evaluation of their use is presented. This work was motivated by, and is a precursor to, an industrial application of Bayes linear modelling of the reliability of defence systems. An illustrative example demonstrates how the methods can be used in practice

Illuminating disease and enlightening biomedicine:Raman spectroscopy as a diagnostic tool

The discovery of the Raman effect in 1928 not only aided fundamental understanding about the quantum nature of light and matter but also opened up a completely novel area of optics and spectroscopic research that is accelerating at a greater rate during the last decade than at any time since its inception. This introductory overview focuses on some of the most recent developments within this exciting field and how this has enabled and enhanced disease diagnosis and biomedical applications. We highlight a small number of stimulating high-impact studies in imaging, endoscopy, stem cell research, and other recent developments such as spatially offset Raman scattering amongst others. We hope this stimulates further interest in this already exciting field, by 'illuminating' some of the current research being undertaken by the latest in a very long line of dedicated experimentalists interested in the properties and potential beneficial applications of light