id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-346611-jyktuvyy	Manski, C. F.	How Should Clinicians Interpret Imprecise Trials Assessing Drugs for COVID-19 Patients?	2020-06-05		.txt	text/plain	8169	527	56	Considering the design of 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. Table 2 compares near-optimality of the empirical success rule and the hypothesis test-based decision criterion in two-arm trials for a wide range of sample sizes. Given any specified sample size, the empirical success rule has been shown to achieve the lowest possible value of near-optimality in trials with binary outcomes that assign an equal number of patients to each arm (Stoye, 2009 ). In Table 4 we compare near-optimality of prescribing treatments using standard multiple hypothesis testing approach and of prescribing them using the empirical success rule in five-arm trials with different sample sizes. Manski and Tetenov (2019) study the near-optimality of the empirical success rule when there are two feasible treatments and patient welfare is a weighted sum of binary primary and secondary outcomes.	./cache/cord-346611-jyktuvyy.txt	./txt/cord-346611-jyktuvyy.txt