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来源类型Working Paper
规范类型报告
DOI10.3386/w27293
来源IDWorking Paper 27293
Statistical Decision Properties of Imprecise Trials Assessing COVID-19 Drugs
Charles F. Manski; Aleksey Tetenov
发表日期2020-06-08
出版年2020
语种英语
摘要As the COVID-19 pandemic progresses, researchers are reporting findings of randomized trials comparing standard care with care augmented by experimental drugs. The trials have small sample sizes, so estimates of treatment effects are imprecise. Seeing imprecision, clinicians reading research articles may find it difficult to decide when to treat patients with experimental drugs. Whatever decision criterion one uses, there is always some probability that random variation in trial outcomes will lead to prescribing sub-optimal treatments. A conventional practice when comparing standard care and an innovation is to choose the innovation only if the estimated treatment effect is positive and statistically significant. This practice defers to standard care as the status quo. To evaluate decision criteria, we use the concept of near optimality, which jointly considers the probability and magnitude of decision errors. An appealing decision criterion from this perspective is the empirical success rule, which chooses the treatment with the highest observed average patient outcome in the trial. Considering the design of recent and ongoing COVID-19 trials, we show that the empirical success rule yields treatment results that are much closer to optimal than those generated by prevailing decision criteria based on hypothesis tests.
主题Econometrics ; Estimation Methods ; Health, Education, and Welfare ; Health ; COVID-19
URLhttps://www.nber.org/papers/w27293
来源智库National Bureau of Economic Research (United States)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/584965
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Charles F. Manski,Aleksey Tetenov. Statistical Decision Properties of Imprecise Trials Assessing COVID-19 Drugs. 2020.
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