Transparency in decision modelling is an evolving concept. Recently, discussion has moved from reporting standards to open source implementation of decision analytic models. However, in the debate about the supposed advantages and disadvantages of greater transparency, there is a lack of definition. The purpose of this article is not to present a case for or against transparency, but rather to provide a more nuanced understanding of what transparency means in the context of decision modelling and how it could be addressed. To this end, we review and summarise the discourse to date, drawing on our collective experience. We outline a taxonomy of the different manifestations of transparency, including reporting standards, reference models, collaboration, model registration, peer review, and open source modelling. Further, we map out the role and incentives for the various stakeholders, including industry, research organisations, publishers, and decision-makers. We outline the anticipated advantages and disadvantages of greater transparency with respect to each manifestation, as well as the perceived barriers and facilitators to greater transparency. These are considered with respect to the different stakeholders and with reference to issues including intellectual property, legality, standards, quality assurance, code integrity, health technology assessment processes, incentives, funding, software, access and deployment options, data protection, and stakeholder engagement. For each manifestation of transparency, we discuss the 'what', 'why', 'who', and 'how'. Specifically, their meaning, why the community might (or might not) wish to embrace them, whose engagement as stakeholders is required, and how relevant objectives might be realised. We identify current initiatives aimed to improve transparency to exemplify efforts in current practice and for the future. KEY POINTS FOR DECISION-MAKERS There is a variety of manifestations of transparency in decision modelling, ranging in the amount of information and accessibility of information that they tend to provide. There is a broad array of stakeholders who create, manage, influence, evaluate, use, or are otherwise affected by, decision models. Given the range of stakeholders and interests related to increased transparency of decision models, achieving the benefits while managing the risks requires careful consideration. Issues such as intellectual property, legal matters, funding, use, and sharing of software need to be addressed.
There is unmet need in patients suffering from chronic pain, yet innovation may be impeded by the difficulty of justifying economic value in a field beset by data limitations and methodological variability. A systematic review was conducted to identify and summarise the key areas of variability and limitations in modelling approaches in the economic evaluation of treatments for chronic pain. The results of the literature review were then used to support the development of a fully flexible open-source economic model structure, designed to test structural and data assumptions and act as a reference for future modelling practice. The key model design themes identified from the systematic review included: time horizon; titration and stabilisation; number of treatment lines; choice/ordering of treatment; and the impact of parameter uncertainty (given reliance on expert opinion). Exploratory analyses using the model to compare a hypothetical novel therapy versus morphine as first-line treatments showed cost-effectiveness results to be sensitive to structural and data assumptions. Assumptions about the treatment pathway and choice of time horizon were key model drivers. Our results suggest structural model design and data assumptions may have driven previous cost-effectiveness results and ultimately decisions based on economic value. We therefore conclude that it is vital that future economic models in chronic pain are designed to be fully transparent and hope our open-source code is useful in order to aspire to a common approach to modelling pain that includes robust sensitivity analyses to test structural and parameter uncertainty.Electronic supplementary materialThe online version of this article (doi:10.1007/s10198-015-0720-y) contains supplementary material, which is available to authorized users.
Objectives: Complexities in the neuropathic-pain care pathway make the condition difficult to manage and difficult to capture in cost-effectiveness models. The aim of this study is to understand, through a systematic review of previous cost-effectiveness studies, some of the key strengths and limitations in data and modeling practices in neuropathic pain. Thus, the aim is to guide future research and practice to improve resource allocation decisions and encourage continued investment to find novel and effective treatments for patients with neuropathic pain. Methods: The search strategy was designed to identify peer-reviewed cost-effectiveness evaluations of non-surgical, pharmaceutical therapies for neuropathic pain published since January 2000, accessing five key databases. All identified publications were reviewed and screened according to pre-defined eligibility criteria. Data extraction was designed to reflect key data challenges and approaches to modeling in neuropathic pain and based on published guidelines. Results: The search strategy identified 20 cost-effectiveness analyses meeting the inclusion criteria, of which 14 had original model structures. Cost-effectiveness modeling in neuropathic pain is established and increasing across multiple jurisdictions; however, amongst these studies, there is substantial variation in modeling approach, and there are common limitations. Capturing the effect of treatments upon health outcomes, particularly health-related quality-of-life, is challenging, and the health effects of multiple lines of ineffective treatment, common for patients with neuropathic pain, have not been consistently or robustly modeled. Conclusions: To improve future economic modeling in neuropathic pain, further research is suggested into the effect of multiple lines of treatment and treatment failure upon patient outcomes and subsequent treatment effectiveness; the impact of treatment-emergent adverse events upon patient outcomes; and consistent and appropriate pain measures to inform models. The authors further encourage transparent reporting of inputs used to inform cost-effectiveness models, with robust, comprehensive and clear uncertainty analysis and, where feasible, open-source modeling is encouraged. ARTICLE HISTORY
Teicoplanin is a new glycopeptide antibiotic, active against aerobic and anaerobic gram-positive bacteria. The drug is intended for the treatment of systemic infections including endocarditis. In two U.S. clinical safety and efficacy trials, loading doses of 6 to 30 mg/kg doses of teicoplanin were administered initially to 197 patients, followed by once-a-day treatment of approximately the same doses over several weeks. Blood samples were collected sporadically during the study to monitor serum teicoplanin concentrations either by FPIA or microbiological assay. Nonlinear mixed-effects modeling was performed on these data to characterize the population pharmacokinetics of teicoplanin that were best described by a two-compartment model. Patient body weight, concomitant gram-positive drug treatment, and serum creatinine had significant influences on systemic clearance (CL) of the glycopeptide. In addition, body weight affected the volume of distribution of the central compartment (Vc). Other demographic factors such as age, gender, etc., had no effects. The FPIA assay method was more precise than the microbiological assay.
ObjectiveThe aim was to determine the cost effectiveness of secukinumab, a fully human interleukin-17A inhibitor, for adults in the UK with active psoriatic arthritis (PsA) who are tumour necrosis factor inhibitor (TNFi) naïve and without concomitant moderate-to-severe psoriasis, and who have responded inadequately to conventional systemic disease-modifying anti-rheumatic drugs (csDMARDs).Perspective and settingThe study took the perspective and setting of the UK National Health Service (NHS).MethodsThe model structure was a 3-month decision tree leading into a Markov model. Separate analyses based on the number of prior csDMARDs (one and two or more) were conducted, with secukinumab 150 mg compared to standard of care (SoC) and TNFis, respectively, for each subpopulation. Clinical parameters, including response at 3 months, were from the FUTURE 2 trial and a network meta-analysis. Outcomes included total costs and quality-adjusted life years (QALYs) over the 40-year time horizon (3.5% annual discount for both outcomes; cost year 2017), and incremental cost effectiveness ratios (ICERs).ResultsThe ICER for secukinumab 150 mg versus SoC was £28,748 per QALY gained (one prior csDMARD). Secukinumab 150 mg dominated golimumab, certolizumab pegol and etanercept, and had an ICER of £5680 per QALY gained versus adalimumab and > £1 million saved per QALY foregone versus infliximab (two or more prior csDMARDs). Valuing one QALY at between £20,000 and £30,000, the probability of secukinumab having the highest net monetary benefit was 48.9% (one prior csDMARD) and 88.9% (two or more prior csDMARDs). Parameters related to Health Assessment Questionnaire scores were most influential.ConclusionsSecukinumab 150 mg at list price appears to represent a cost-effective use of NHS resources for adults with PsA who have responded inadequately to one or two or more prior csDMARDs.Electronic supplementary materialThe online version of this article (10.1007/s40273-018-0674-x) contains supplementary material, which is available to authorized users.
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