全球智库信息集成服务系统(G2TT): Bayesian Adaptive Clinical Trials for Anti\u2010Infective Therapeutics during Epidemic Outbreaks

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/w27175
来源ID	Working Paper 27175
	Bayesian Adaptive Clinical Trials for Anti\u2010Infective Therapeutics during Epidemic Outbreaks
	Shomesh Chaudhuri; Andrew W. Lo; Danying Xiao; Qingyang Xu
发表日期	2020-05-18
出版年	2020
语种	英语
摘要	In the midst of epidemics such as COVID-19, therapeutic candidates are unlikely to be able to complete the usual multiyear clinical trial and regulatory approval process within the course of an outbreak. We apply a Bayesian adaptive patient-centered model—which minimizes the expected harm of false positives and false negatives—to optimize the clinical trial development path during such outbreaks. When the epidemic is more infectious and fatal, the Bayesian-optimal sample size in the clinical trial is lower and the optimal statistical significance level is higher. For COVID-19 (assuming a static R₀ – 2 and initial infection percentage of 0.1%), the optimal significance level is 7.1% for a clinical trial of a nonvaccine anti-infective therapeutic and 13.6% for that of a vaccine. For a dynamic R₀ decreasing from 3 to 1.5, the corresponding values are 14.4% and 26.4%, respectively. Our results illustrate the importance of adapting the clinical trial design and the regulatory approval process to the specific parameters and stage of the epidemic.
主题	Econometrics ; Estimation Methods ; Experimental Design ; Public Economics ; National Fiscal Issues ; Health, Education, and Welfare ; Health ; COVID-19
URL	https://www.nber.org/papers/w27175
来源智库	National Bureau of Economic Research (United States)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/584848
推荐引用方式 GB/T 7714	Shomesh Chaudhuri,Andrew W. Lo,Danying Xiao,et al. Bayesian Adaptive Clinical Trials for Anti\u2010Infective Therapeutics during Epidemic Outbreaks. 2020.

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w27175.pdf（660KB）	智库出版物		限制开放	CC BY-NC-SA	浏览