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Table 2 Abstracted data from included studies

From: Early economic evaluation of emerging health technologies: protocol of a systematic review

  Values Source
Decision context   
 Who initiate the evaluation? For example, policy decision makers, industryb, third-party payers, hospital managers/administrators, clinicians, patients or patient advocacy groups, not reported [27]
 Who commissioned it? For example, policy decision makers, industryb, third-party payers, hospital managers/administrators, clinicians, patients or patient advocacy groups, not reported [27]
 What are the funding sources of the study? For example, public, private, both, others, not reported [27]
 Why is an evaluation needed right now? Informing product development, informing policies, not reported [27]
 Who conducted the study? For example, academia, public, private, multiple sources, others, not reported [27]
 Primary target audience according to the decision context For example, policy decision makers, industryb, third-party payers, hospital managers/administrators, clinicians, patients or patient advocacy groups, not reported [27]
Defining the decision problem   
 Decision problem Open coding. Decision problems will be classified. For example, the primary reason for initiating the early evaluation could be product development, health policy [28, 29]
 Disease or clinical problem ICD-10 major categories (see listing in footnotes) [30]
 Target population For example, patients, at-risk individuals, general public, unclear [29]
Health technology   
 Technology type For example, pharmaceuticals, medical devices, diagnostic devices, other therapeutic technologies [28]
 Technology stage For example, basic, translational research, clinical researchc [8]
Methodological aspects of the EE   
 Perspective For example, societal, publicly funded health-care system, third-party payers (e.g., insurance companies), not reported [28, 29]
 Type of evaluations For example, CEA, CUA, CMA, CBA, others [28, 29]
 Basis of the evaluation For example, trial-based, model-based, prospective or retrospective analysis, others [29]
 Time horizon Numerical number, not reported [28, 29]
 Adjusted for differential timing? Numerical percentage, not reported [28, 29]
Likely effect of the new technology   
 Quantification of the likely effecta Open coding to identify data sources, methods, assumptionsd [28, 29]
Valuing health outcomes   
 Valuation of the likely effecta Open coding to identify data sources, methods, assumptionsd [28, 29]
Resources used and costs   
 Quantification of the likely impact on resourcesa Open coding to identify data sources, methods, assumptionsd [28, 29]
Modeling, if appropriate   
 Model type For example, decision tree, state transition (e.g., Markov), discrete event simulation, dynamic transition model, others [28, 29]
Handling of variability and uncertainty   
 Scenario analysis (e.g., structural uncertainty) Yes, no [28, 29]
 Sensitivity analysis (e.g., one-way, multiple-way) Yes, no [28, 29]
 Probabilistic sensitivity analysis Yes, no [28, 29]
 Value-of-information analysis Yes, no [28, 29]
Presenting results of the economic evaluation   
 According to the authors, are results likely to be influential to the decision problem? Yes, no, unclear [28, 29]
 According to the reviewers, are results likely to be influential to the decision problem? Yes, no, unclear [28, 29]
  1. aDefinition of treatment effect of a technology can be summarized as the difference between the duration and state of health or HRQL (including the impact of any adverse effects of treatment) that would be experienced on average by patients receiving the technology and that experienced by the same group were they to receive alternative care; bindustry would include pharmaceutical, device, or genomic manufacturers; investors or inventors;ctechnologies are ready for diffusion after regulatory approval, certification of laboratory quality, or marketing authorization; dtext coding and review of text descriptions across included studies to derive a broad classification for data sources (e.g., expert opinion, individual patient data, administrative databases, clinical registries), methods used (e.g., systematic review, meta-analysis, RCT, non-RCT) and types of assumptions (e.g., based upon basic science, early clinical experience). CEA cost-effectiveness analysis, CUA cost-utility analysis, CMA cost-minimization analysis, CBA cost-benefit analysis, QALY quality-adjusted life years, HRQOL health-related quality of life, ICER incremental cost-effectiveness ratio, CE cost-effectiveness, PSA probabilistic sensitivity analysis.