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Internal validity is often the primary concern for health technology assessment agencies when assessing comparative effectiveness evidence. However, the increasing use of real-world data from countries other than a health technology assessment agency's target population in effectiveness research has increased concerns over the external validity, or "transportability", of this evidence, and has led to a preference for local data. Methods have been developed to enable a lack of transportability to be addressed, for example by accounting for cross-country differences in disease characteristics, but their consideration in health technology assessments is limited. This may be because of limited knowledge of the methods and/or uncertainties in how best to utilise them within existing health technology assessment frameworks. This article aims to provide an introduction to transportability, including a summary of its assumptions and the methods available for identifying and adjusting for a lack of transportability, before discussing important considerations relating to their use in health technology assessment settings, including guidance on the identification of effect modifiers, guidance on the choice of target population, estimand, study sample and methods, and how evaluations of transportability can be integrated into health technology assessment submission and decision processes.
Assuntos
Avaliação da Tecnologia Biomédica , Humanos , IncertezaRESUMO
PURPOSE: To estimate the healthcare resource utilization (HCRU) and costs for patients with normal tension glaucoma (NTG) as well as their payers across various levels of disease severity. PATIENTS AND METHODS: Our study was a retrospective cohort study of 6330 US NTG patients. Patients were enrolled if they were 40 years or older and had two or more qualifying NTG diagnoses within the enrollment period, October 1st, 2015 to December 31st, 2017. Our analysis was carried out for two cohorts - those with unilateral disease and those with bilateral disease. Baseline demographic and clinical characteristics were assessed for a 12-month pre-index period. The follow up period was 12-months post-index. We employed generalized linear models to model HCRU and costs. RESULTS: Patients with severe, bilateral disease, filled more than two additional prescriptions annually (2.5, p<0.001, 95% CI [2.0, 3.1]) when compared to their mild counterparts and accounted for 111 (p<0.001, 95% CI [83.5, 139.1]) extra days of supply of glaucoma medications. These patients face an adjusted $187 (p<0.001, 95% CI [145, 229]) more out-of-pocket (OOP), and payers an additional $598 (p<0.001, 95% CI [$370, $826]), than their counterparts with a mild diagnosis on an annual basis. Total annual payer costs, on average (SD) for those with severe bilateral NTG were $1175 ($2222). CONCLUSION: Our results suggest that patient and payer burden is significantly greater for those with severe disease compared to those with mild NTG. The excess burden is attributed to additional HCRU and the associated financial burden. Payers experienced a much larger financial burden from patients with severe disease compared to those with mild NTG. Approximately half of the cost differences can be attributed to additional prescription use.
RESUMO
Results of randomized controlled trials (RCTs) provide valuable comparisons of 2 or more interventions to inform health care decision making; however, many more comparisons are required than available time and resources to conduct them. Moreover, RCTs have limited generalizability. Comparative effectiveness research (CER) using real-world evidence (RWE) can increase generalizability and is important for decision making, but use of nonrandomized designs makes their evaluation challenging. Several tools are available to assist. In this study, we comparatively characterize 5 tools used to evaluate RWE studies in the context of making health care adoption decision making: (1) Good Research for Comparative Effectiveness (GRACE) Checklist, (2) IMI GetReal RWE Navigator (Navigator), (3) Center for Medical Technology Policy (CMTP) RWE Decoder, (4) CER Collaborative tool, and (5) Real World Evidence Assessments and Needs Guidance (REAdi) tool. We describe each and then compare their features along 8 domains: (1) objective/user/context, (2) development/scope, (3) platform/presentation, (4) user design, (5) study-level internal/external validity of evidence, (6) summarizing body of evidence, (7) assisting in decision making, and (8) sharing results/making improvements. Our summary suggests that the GRACE Checklist aids stakeholders in evaluation of the quality and applicability of individual CER studies. Navigator is a collection of educational resources to guide demonstration of effectiveness, a guidance tool to support development of medicines, and a directory of authoritative resources for RWE. The CMTP RWE Decoder aids in the assessment of relevance and rigor of RWE. The CER Collaborative tool aids in the assessment of credibility and relevance. The REAdi tool aids in refinement of the research question, study retrieval, quality assessment, grading the body of evidence, and prompts with questions to facilitate coverage decisions. All tools specify a framework, were designed with stakeholder input, assess internal validity, are available online, and are easy to use. They vary in their complexity and comprehensiveness. The RWE Decoder, CER Collaborative tool, and REAdi tool synthesize evidence and were specifically designed to aid formulary decision making. This study adds clarity on what the tools provide so that the user can determine which best fits a given purpose. DISCLOSURES: This work was supported by the Health Tech Fund, which was provided to the University of Washington School of Pharmacy by its Corporate Advisory Board. This consortium of pharmaceutical and biotech companies supports the research program of the University of Washington School of Pharmacy across the competitive space. The sponsors seeded the idea for the project and contributed to study design and improvement. The authors had full control of all content development, manuscript drafting, and submission for publication. The REAdi tool was developed by the authors. Chen, Bansal, Barthold, Carlson, Veenstra, Basu, Devine, Yun, Ta, and Beal were supported by a training grant from the University of Washington-Allergan Fellowship, unrelated to this work. Basu reports personal fees from Salutis Consulting, unrelated to this work. Graff is an employee of the National Pharmaceutical Council, which was a partner in the development of the CER Collaborative and funding partner for the CMTP RWE Decoder and the GRACE Checklist. A previous version of this work was presented as an invited workshop at AMCP Nexus 2018; October 22-25, 2018; Orlando, FL.