Introduction Approaches to economic evaluations of stroke therapies are varied and inconsistently described. An objective of the European Stroke Organisation (ESO) Health Economics Working Group is to standardise and improve the economic evaluations of interventions for stroke. Methods The ESO Health Economics Working Group and additional experts were contacted to develop a protocol and a guidance document for data collection for economic evaluations of stroke therapies. A modified Delphi approach, including a survey and consensus processes, was used to agree on content. We also asked the participants about resources that could be shared to improve economic evaluations of interventions for stroke. Results Of 28 experts invited, 16 (57%) completed the initial survey, with representation from universities, government, and industry. More than half of the survey respondents endorsed 13 specific items to include in a standard resource use questionnaire. Preferred functional/quality of life outcome measures to use for economic evaluations were the modified Rankin Scale (14 respondents, 88%) and the EQ-5D instrument (11 respondents, 69%). Of the 12 respondents who had access to data used in economic evaluations, 10 (83%) indicated a willingness to share data. A protocol template and a guidance document for data collection were developed and are presented in this article. Conclusion The protocol template and guidance document for data collection will support a more standardised and transparent approach for economic evaluations of stroke care.
Objective and Background The clinical trials community has been hesitant to adopt Bayesian statistical methods, which are often more flexible and efficient with more naturally interpretable results than frequentist methods. We aimed to identify self-reported barriers to implementing Bayesian methods and preferences for becoming comfortable with them. Methods We developed a 22-question survey submitted to medical researchers (non-statisticians) from industry, academia, and regulatory agencies. Question areas included demographics, experience, comfort levels with Bayesian analyses, perceived barriers to these analyses, and preferences for increasing familiarity with Bayesian methods. Results Of the 323 respondents, most were affiliated with pharmaceutical companies (33.4%), clinical research organizations (29.7%), and regulatory agencies (18.6%). The rest represented academia, medical practice, or other. Over 56% of respondents expressed little to no comfort in interpreting Bayesian analyses. “Insufficient knowledge of Bayesian approaches” was ranked the most important perceived barrier to implementing Bayesian methods by a plurality (48%). Of the approaches listed, in-person training was the most preferred for gaining comfort with Bayesian methods. Conclusions Based on these survey results, we recommend that introductory level training on Bayesian statistics be presented in an in-person workshop that could also be broadcast online with live Q&A. Other approaches such as online training or collaborative projects may be better suited for higher-level trainings where instructors may assume a baseline understanding of Bayesian statistics. Increased coverage of Bayesian methods at medical conferences and medical school trainings would help improve comfort and overcome the substantial knowledge barriers medical researchers face when implementing these methods. Supplementary Information The online version contains supplementary material available at 10.1007/s43441-021-00357-x.
Medical investigations for therapeutics and vaccines for combating a pandemic such as COVID-19, call for flexible and adaptive trial designs that are capable of producing robust results amidst uncertainties. Here we present a Bayesian sequential design to study the efficacy of Bacillus Calmette-Guérin (BCG) in providing protection against COVID-19 infections via its known "trained-immunity" mechanism. The main design consideration is to provide a framework to rapidly establish a proof-of-concept on the vaccine efficacy of BCG under a constantly evolving incidence rate and in absence of prior efficacy data. The trial design is based on taking several interim looks and calculating the predictive power with the current cohort at each interim look. Decisions to stop the trial for futility or stopping enrollment for efficacy are made based on the current cohort predictive power computation. At any interim, if any of the above decisions cannot be taken then the study continues to enroll till the next interim look. Via extensive numerical studies, we show that the proposed design can achieve the desired frequentist operating characteristics, currently required by regulatory bodies while offering greater flexibility in terms of sample size and the ability to make robust interim decisions.
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