Jathishinie Jegathisawaran

What Influences the Cost Effectiveness of Dabigatran versus Warfarin for Stroke Prevention in Atrial Fibrillation: A Systematic Review

Objectives The introduction of new oral anticoagulants for the prevention of stroke in atrial fibrillation (AF) has changed the clinical management of AF. To inform decision making around dabigatran by identifying factors influencing cost-effectiveness results, we undertook a systematic review of economic evaluations of dabigatran versus warfarin for the prevention of stroke in AF patients. Methods A systematic literature search of Ovid Medline and Embase, Wileys Cochrane Library, HEED, PubMed databases and grey literature was carried out for primary economic evaluations comparing dabigatran versus warfarin in patients with AF. Data on study characteristics, model inputs and results, and sensitivity analyses were abstracted and synthesized qualitatively. Results Twenty-three economic evaluations were identified and RE-LY was cited in 52% of studies as the source of the efficacy data. Twenty evaluations used Markov modelling, 2 performed discrete event simulation, and 1 was a trial-based evaluation. Eighty-two percent reported base case incremental cost-effectiveness ratios (ICERs) of less than

Do different decision-analytic modeling approaches produce different results? A systematic review of cross-validation studies

50,000 USD/QALY. Key variables, including international normalized ratio (INR) control, the cost of monitoring, risk of stroke and bleeding, and age were found to alter the conclusions in only a few studies. Less commonly explored factors included time horizon and cost of long-term care follow-up. Conclusions Several factors should be considered when interpreting the results of economic analyses which are based on randomized clinical trial evidence. Real-world data are needed to further assess the clinical and economic consequences of dabigatran relative to warfarin for the prevention of stroke in AF.

Systematic narrative review of decision frameworks to select the appropriate modelling approaches for health economic evaluations

When choosing a modeling approach for health economic evaluation, certain criteria are often considered (e.g., population resolution, interactivity, time advancement mechanism, resource constraints). However, whether these criteria and their associated modeling approach impacts results remain poorly understood. A systematic review was conducted to identify cross-validation studies (i.e., modeling a problem using different approaches with the same body of evidence) to offer insight on this topic. With respect to population resolution, reviewed studies suggested that both aggregate- and individual-level models will generate comparable results, although a practical trade-off exists between validity and feasibility. In terms of interactivity, infectious-disease models consistently showed that, depending on the assumptions regarding probability of disease exposure, dynamic and static models may produce dissimilar results with opposing policy recommendations. Empirical evidence on the remaining criteria is limited. Greater discussion will therefore be necessary to promote a deeper understanding of the benefits and limits to each modeling approach.

Economic burden of illness associated with diabetic foot ulcers in Canada

BackgroundIn constructing or appraising a health economic model, an early consideration is whether the modelling approach selected is appropriate for the given decision problem. Frameworks and taxonomies that distinguish between modelling approaches can help make this decision more systematic and this study aims to identify and compare the decision frameworks proposed to date on this topic area.MethodsA systematic review was conducted to identify frameworks from peer-reviewed and grey literature sources. The following databases were searched: OVID Medline and EMBASE; Wiley’s Cochrane Library and Health Economic Evaluation Database; PubMed; and ProQuest.ResultsEight decision frameworks were identified, each focused on a different set of modelling approaches and employing a different collection of selection criterion. The selection criteria can be categorized as either: (i) structural features (i.e. technical elements that are factual in nature) or (ii) practical considerations (i.e. context-dependent attributes). The most commonly mentioned structural features were population resolution (i.e. aggregate vs. individual) and interactivity (i.e. static vs. dynamic). Furthermore, understanding the needs of the end-users and stakeholders was frequently incorporated as a criterion within these frameworks.ConclusionsThere is presently no universally-accepted framework for selecting an economic modelling approach. Rather, each highlights different criteria that may be of importance when determining whether a modelling approach is appropriate. Further discussion is thus necessary as the modelling approach selected will impact the validity of the underlying economic model and have downstream implications on its efficiency, transparency and relevance to decision-makers.