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Background and Objective: Decision models for health technology assessment (HTA) are largely submitted to HTA agencies using commercial software, which has known limitations. The use of the open-source programming language R has been suggested because of its efficiency, transparency, reproducibility, and ability to consider complex analyses. However, its use in HTA remains limited. This qualitative study aimed to explore the main reasons for this slow uptake of R in HTA and identify tangible facilitators. Methods: We undertook two semi-structured focus group discussions with 24 key stakeholders from government agencies, consultancy, pharmaceutical companies, and academia. Two 1.5-hour discussions reflected on barriers identified in a previous study and highlighted additional barriers. Discussions were recorded and semi-transcribed, and data were organized and summarized into key themes. Results: Human resources constraints were identified as a key barrier, including a lack of training, prioritization and collaboration, and resistance to change. Another key barrier was the lack of acceptance, or clear guidance, around submissions in R by HTA agencies. Participants also highlighted a lack of communication around accepted packages and decision model structures, and between HTA agencies on standard decision modeling structures. Conclusions: There is a need for standardization, which can facilitate decision model sharing, coding homogeneity, and improved country adaptations. The creation of training materials and tailored workshops was identified as a key short-term facilitator. Increased communication and engagement of stakeholders could also facilitate the use of R by identifying needs and opportunities, encouraging HTA agencies to address structural barriers, and increasing incentives to use R. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to provide clinicians with introductory guidance for interpreting and assessing confidence in on Network meta-analysis (NMA) results. Methods: We reviewed current literature on NMA and summarized key points. Results: Network meta-analysis (NMA) is a statistical method for comparing the efficacy of three or more interventions simultaneously in a single analysis by synthesizing both direct and indirect evidence across a network of randomized clinical trials. It has become increasingly popular in healthcare, since direct evidence (head-to-head randomized clinical trials) are not always available. NMA methods are categorized as either Bayesian or frequentist, and while the two mostly provide similar results, the two approaches are theoretically different and require different interpretations of the results. Conclusions: We recommend a careful approach to interpreting NMA results and the validity of an NMA depends on its underlying statistical assumptions and the quality of the evidence used in the NMA. [ABSTRACT FROM AUTHOR]

Clinical trials require participation of numerous patients, enormous research resources and substantial public funding. Time-consuming trials lead to delayed implementation of beneficial interventions and to reduced benefit to patients. This manuscript discusses two methods for the allocation of research resources and reviews a framework for prioritisation and design of clinical trials. The traditional error-driven approach of clinical trial design controls for type I and II errors. However, controlling for those statistical errors has limited relevance to policy makers. Therefore, this error-driven approach can be inefficient, waste research resources and lead to research with limited impact on daily practice. The novel value-driven approach assesses the currently available evidence and focuses on designing clinical trials that directly inform policy and treatment decisions. Estimating the net value of collecting further information, prior to undertaking a trial, informs a decision maker whether a clinical or health policy decision can be made with current information or if collection of extra evidence is justified. Additionally, estimating the net value of new information guides study design, data collection choices, and sample size estimation. The value-driven approach ensures the efficient use of research resources, reduces unnecessary burden to trial participants, and accelerates implementation of beneficial healthcare interventions. [ABSTRACT FROM AUTHOR]

Exome sequencing and genome sequencing have the potential to improve clinical utility for patients undergoing genetic investigations. However, evidence of clinical utility is limited to pediatric populations; we aimed to fill this gap by conducting a systematic review of the literature on the clinical utility of exome/genome sequencing across disease indications in pediatric and adult populations. MEDLINE, EMBASE and Cochrane Library were searched between 2016 and 2020. Quantitative studies evaluating diagnostic yield were included; other measures of clinical utility such as changes to clinical management were documented if reported. Two reviewers screened, extracted data, and appraised risk of bias. Fifty studies met our inclusion criteria. All studies reported diagnostic yield, which ranged from 3 to 70%, with higher range of yields reported for neurological indications and acute illness ranging from 22 to 68% and 37–70%, respectively. Diagnoses triggered a range of clinical management changes including surveillance, reproductive-risk counseling, and identifying at-risk relatives in 4–100% of patients, with higher frequencies reported for acute illness ranging from 67 to 95%. The frequency of variants of uncertain significance ranged from 5 to 85% across studies with a potential trend of decreasing frequency over time and higher rates identified in patients of non-European ancestry. This review provides evidence for a higher range of diagnostic yield of exome/genome sequencing compared to standard genetic tests, particularly in neurological and acute indications. However, we identified significant heterogeneity in study procedures and outcomes, precluding a meaningful meta-analysis and certainty in the evidence available for decision-making. Future research that incorporates a comprehensive and consistent approach in capturing clinical utility of exome/genome sequencing across broader ancestral groups is necessary to improve diagnostic accuracy and yield and allow for analysis of trends over time. Prospero registration CRD42019094101. [ABSTRACT FROM AUTHOR]

The use of open-source programming languages, such as R, in health decision sciences is growing and has the potential to facilitate model transparency, reproducibility, and shareability. However, realizing this potential can be challenging. Models are complex and primarily built to answer a research question, with model sharing and transparency relegated to being secondary goals. Consequently, code is often neither well documented nor systematically organized in a comprehensible and shareable approach. Moreover, many decision modelers are not formally trained in computer programming and may lack good coding practices, further compounding the problem of model transparency. To address these challenges, we propose a high-level framework for model-based decision and cost-effectiveness analyses (CEA) in R. The proposed framework consists of a conceptual, modular structure and coding recommendations for the implementation of model-based decision analyses in R. This framework defines a set of common decision model elements divided into five components: (1) model inputs, (2) decision model implementation, (3) model calibration, (4) model validation, and (5) analysis. The first four components form the model development phase. The analysis component is the application of the fully developed decision model to answer the policy or the research question of interest, assess decision uncertainty, and/or to determine the value of future research through value of information (VOI) analysis. In this framework, we also make recommendations for good coding practices specific to decision modeling, such as file organization and variable naming conventions. We showcase the framework through a fully functional, testbed decision model, which is hosted on GitHub for free download and easy adaptation to other applications. The use of this framework in decision modeling will improve code readability and model sharing, paving the way to an ideal, open-source world. [ABSTRACT FROM AUTHOR]

Purpose: Obstructive sleep apnea (OSA) is the most common sleep-related breathing disorder. In-laboratory, overnight type I polysomnography (PSG) is the current “gold standard” for diagnosing OSA. Home sleep apnea testing (HSAT) using portable monitors (PMs) is an alternative testing method offering better comfort and lower costs. We aimed to systematically review the evidence on diagnostic ability of type IV PMs compared to PSG in diagnosing OSA.Methods: Participants: patients ≥16 years old with symptoms suggestive of OSA;intervention: type IV PMs (devices with < 2 respiratory channels); comparator: in-laboratory PSG; outcomes: diagnostic accuracy measures;studies: cross-sectional, prospective observational/experimental/quasi-experimental studies; information sources: MEDLINE and Cochrane Library from January 1, 2010 to May 10, 2016. All stages of review were conducted independently by two investigators.Results: We screened 6054 abstracts and 117 full-text articles to select 24 full-text articles for final review. These 24 studies enrolled a total of 2068 patients with suspected OSA and evaluated 10 different PMs with one to six channels. Only seven (29%) studies tested PMs in the home setting. The mean difference (bias) between PSG-measured and PM-measured apnea-hypopnea index (AHI) ranged from − 14.8 to 10.6 events/h. At AHI ≥ 5 events/h, the sensitivity of type IV PMs ranged from 67.5-100% and specificity ranged from 25 to 100%.Conclusion: While current evidence is not very strong for the stand-alone use of level IV PMs in clinical practice, they can potentially widen access to diagnosis and treatment of OSA. Policy recommendations regarding HSAT use should also consider the health and broader social implications of false positive and false negative diagnoses. [ABSTRACT FROM AUTHOR]

The volume and technical complexity of both academic and commercial research using decision analytic modelling has increased rapidly over the last two decades. The range of software programs used for their implementation has also increased, but it remains true that a small number of programs account for the vast majority of cost-effectiveness modelling work. We report a comparison of four software programs: TreeAge Pro, Microsoft Excel, R and MATLAB. Our focus is on software commonly used for building Markov models and decision trees to conduct cohort simulations, given their predominance in the published literature around cost-effectiveness modelling. Our comparison uses three qualitative criteria as proposed by Eddy et al.: "transparency and validation", "learning curve" and "capability". In addition, we introduce the quantitative criterion of processing speed. We also consider the cost of each program to academic users and commercial users. We rank the programs based on each of these criteria. We find that, whilst Microsoft Excel and TreeAge Pro are good programs for educational purposes and for producing the types of analyses typically required by health technology assessment agencies, the efficiency and transparency advantages of programming languages such as MATLAB and R become increasingly valuable when more complex analyses are required. [ABSTRACT FROM AUTHOR]

There has been a large increase in the incidence of invasive fungal infections (IFIs) over the past decades, largely because of the increasing size of the population at risk. One of the major risk groups for IFIs are patients with haematological malignancies treated with cytotoxic chemotherapy or undergoing haematopoietic stem cell transplantation. These IFIs are associated with high morbidity and mortality rates. Consequently, as the diagnosis of IFIs is difficult, antifungal prophylaxis is desirable in high-risk patients. Furthermore, as the economic impact of IFIs is also significant, it is important to assess the cost benefit and cost effectiveness of each prophylactic agent in order to aid decisions concerning which prophylactic agent provides the best value for limited healthcare resources. This article systematically reviews the available pharmacoeconomic evidence regarding antifungal prophylaxis in immunocompromised patients treated for haematological malignancies. Furthermore, specific points of interest concerning economic analyses of antifungal prophylaxis are briefly discussed. Considering the available evidence, antifungal prophylaxis in immunocompromised patients treated for haematological malignancies seems to be an intervention with favourable cost-benefit, cost-effectiveness and costsaving potential. Furthermore, recently introduced antifungal agents seem to be attractive alternatives to fluconazole from a pharmacoeconomic point of view. However, due to wide heterogeneity in patient characteristics, underlying diseases, hospital settings and study methods in the included economic studies, as well as the lack of 'head-to-head' trials, it is difficult to find clear evidence of the economic advantages of a single prophylactic agent. Furthermore, we show that the results of cost-effectiveness analyses are highly dependent on several crucial factors that influence the baseline IFI incidence rates and, therefore, differ per patient population or region. [ABSTRACT FROM AUTHOR]