3,921 research outputs found
Value-based pricing: who should set drug prices in the UK?
No full textWhile the UK's NHS and pharma sector ponder who may end up negotiating drug prices in light of a new white paper, health economists Stuart Carroll, Neil Hawkins and David A Scott contemplate four options which keep NICE very much in the pictur
Placebo at Your Peril
No full textINTRODUCTION: The authors consider alternative mechanisms that might explain placebo responses and their implications for cost-effectiveness modeling. Three alternative placebo mechanisms are examined: a ''regression to the mean'' effect arising from natural variation and the preferential selection of patients with acutely severe disease into clinical trials, a patient expectancy effect specific to the clinical trial setting (Hawthorne effect), and a patient expectancy effect generalizable to routine clinical practice (true placebo effect). METHODS: To estimate cost-effectiveness, the authors needed to generalize from trial data to estimate responses to treatment that they would see in routine clinical practice. They use an example analysis of the cost-effectiveness of adjunct epilepsy treatments to illustrate the potential effects of these different placebo mechanisms on this generalization and subsequent cost-effectiveness estimates and adoption decisions. RESULTS: If an acceptable willingness-to-pay threshold of 30,000 per quality-adjusted life year (QALY) is assumed, then each of the placebo effect scenarios identifies a different treatment alternative as being optimum. DISCUSSION: Estimated cost-effectiveness ratios and associated policy decisions may be sensitive to assumptions regarding the mechanism underlying placebo responses. These assumptions should, if possible, be investigated through analysis of trial or observational data and, in the absence of other evidence, sensitivity analysis
Reimbursement and value‐based pricing: stratified cost‐effectiveness analysis may not be the last word
No full textDuring recent discussions, it has been argued that stratified cost-effectiveness analysis has a key role in reimbursement decision‐making and value‐based pricing (VBP). It has previously been shown that when manufacturers are price‐takers, reimbursement decisions made in reference to stratified cost‐effectiveness analysis lead to a more efficient allocation of resources than decisions based on whole‐population cost‐effectiveness analysis. However, we demonstrate that when manufacturers are price setters, reimbursement or VBP based on stratified cost‐effectiveness analysis may not be optimal. Using two examples – one considering the choice of thrombolytic treatment for specific patient subgroups and the other considering the extension of coverage for a cancer treatment to include an additional indication – we show that combinations of extended coverage and reduced price can be identified that are advantageous to both payers and manufacturers. The benefits of a given extension in coverage and reduction in price depend both upon the average treatment benefit in the additional population and its size relative to the original population. Negotiation regarding trade‐offs between price and coverage may lead to improved outcomes both for health‐care systems and manufacturers compared with processes where coverage is determined conditional simply on stratified cost‐effectiveness at a given price. Copyright (C) 2010 John Wiley & Sons, Ltd.value‐based pricing , stratified cost‐effectiveness analysis ,
Facing the Future: the Changing Shape of Academic Skills Support at Bournemouth University
No full textThis paper explores the potential impact of changes to higher education in England on student expectations, engagement, lifestyles and diversity, and outlines implications for the development of digital literacy within academic skills support at Bournemouth University (BU). We will investigate how tackling resource constraints with organisational change can also enable efficient, centralised provision of support materials that utilise networks to overcome the risk of fragmented support for digital literacy. We will also look at how changing delivery modes for support can accommodate changing student lifestyles whilst tackling a weakness of centralised support for digital literacy: that it can become detached from the student’s subject-focused academic practice. Finally we will explore how involving students in developing support can help us to face changes to student expectations and engagement whilst ensuring that materials are authentic and speak to learners in their own voice
Neil Armstrong, standing with two Purdue administrators
No full textLeft to right: Dean George Hawkins, George Mueller, Neil Armstrong
‘Arm-based’ parameterization for network meta-analysis
No full textWe present an alternative to the contrast‐based parameterization used in a number of publications for network meta‐analysis. This alternative “arm‐based” parameterization offers a number of advantages: it allows for a “long” normalized data structure that remains constant regardless of the number of comparators; it can be used to directly incorporate individual patient data into the analysis; the incorporation of multi‐arm trials is straightforward and avoids the need to generate a multivariate distribution describing treatment effects; there is a direct mapping between the parameterization and the analysis script in languages such as WinBUGS and finally, the arm‐based parameterization allows simple extension to treatment‐specific random treatment effect variances.
We validated the parameterization using a published smoking cessation dataset. Network meta‐analysis using arm‐ and contrast‐based parameterizations produced comparable results (with means and standard deviations being within +/− 0.01) for both fixed and random effects models. We recommend that analysts consider using arm‐based parameterization when carrying out network meta‐analyses
Response to Comments on “No Study Left Behind: A Network Meta-Analysis in Non–Small-Cell Lung Cancer Demonstrating the Importance of Considering All Relevant Data”
No full textHow far do you go? Efficient searching for indirect evidence.
No full textBACKGROUND: Indirect evidence is particularly valuable in health care decision making when direct trial evidence comparing relevant treatments is absent or limited. Current approaches using a predetermined set of comparators in the search query may fail to identify all relevant indirect evidence. PURPOSE: To present a framework for the efficient design of search strategies for identifying clinical trials providing indirect evidence for a treatment comparison. FINDINGS: The authors present 2 search strategies that differ from traditional search strategies in using a series of iterative searches to identify the set of relevant comparators. In both, the comparators included in each search are determined by the results of previous searches. For a given number of searches, the strategies presented will find all indirect comparisons that include a certain number of comparators linking the treatments of interest. Methods of estimating the value of indirect evidence via a given number of comparators linking the treatments of interest are presented, thus allowing the burden of additional searching to be traded off against the likely impact of finding more distant comparisons. A practical illustration of the search strategies in the context of informing a network meta-analysis of second-line treatments for non-small cell lung cancer is presented. CONCLUSIONS: The iterative strategies presented offer a means of identifying such evidence and allow the researcher to determine the optimal scope of the search by estimating the value of additional indirect evidence
Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials: a tutorial.
No full textBACKGROUND: Data on survival endpoints are usually summarised using either hazard ratio, cumulative number of events, or median survival statistics. Network meta-analysis, an extension of traditional pairwise meta-analysis, is typically based on a single statistic. In this case, studies which do not report the chosen statistic are excluded from the analysis which may introduce bias. METHODS: In this paper we present a tutorial illustrating how network meta-analyses of survival endpoints can combine count and hazard ratio statistics in a single analysis on the hazard ratio scale. We also describe methods for accounting for the correlations in relative treatment effects (such as hazard ratios) that arise in trials with more than two arms. Combination of count and hazard ratio data in a single analysis is achieved by estimating the cumulative hazard for each trial arm reporting count data. Correlation in relative treatment effects in multi-arm trials is preserved by converting the relative treatment effect estimates (the hazard ratios) to arm-specific outcomes (hazards). RESULTS: A worked example of an analysis of mortality data in chronic obstructive pulmonary disease (COPD) is used to illustrate the methods. The data set and WinBUGS code for fixed and random effects models are provided. CONCLUSIONS: By incorporating all data presentations in a single analysis, we avoid the potential selection bias associated with conducting an analysis for a single statistic and the potential difficulties of interpretation, misleading results and loss of available treatment comparisons associated with conducting separate analyses for different summary statistics
Why Privacy Matters: An Interview with Neil Richards
No full textProfessor Daniel J. Solove discusses the book \u27Why Privacy Matters\u27 and the future of privacy with the author, Professor Neil Richards
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