WHEN TO STOP A CLINICAL-TRIAL
WHEN TO STOP A CLINICAL-TRIAL
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DOI：
10.1136/bmj.305.6847.235
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发表时间：
1992-07-25
影响因子：
105.7
通讯作者：
POCOCK, SJ
中科院分区：
文献类型：
--
作者：
POCOCK, SJ
Most randomised clinical trials require periodic monitoring of the accumulating data. While the efficiency of trial management is enhanced by data monitoring, ethicalreasons should primarily dictate the need to terminate or change a trial in response to interim findings. This article focuses on the ethical dilemma of when to stop a clinical trial and places statistical stopping rules in the context of such ethical decision making. Other issues include the organisation of data monitor-ing committees and the problems ofpremature publication and exaggerated estimation in trials that stop early. Several topical examples are used to convey the relevance of these issues to current practice.The ethical dilemma The basic ethical conflict in monitoring trial results is to balance theinterests of patients within the trial-that is, the individual ethics of randomising the next patient-and the longer term interest of obtaining reliable conclusions on sufficient data-that is, the collective ethics of making appropriate treatment policies for future patients. One common misperception is to concentrate exclusively on individual ethics. For instance, suppose a trial has randomised two patients, one to treatment A who died and one to treatment B who was still alive. Extreme use of individual ethics requires that the next patient should receive full information, including these findings. If there was no other information to dis-tinguish between treatments such" ethical" revelation could make it difficult to randomise the next patient (" I personally would like treatment B, please"). However, it is impossible to reach reliable conclusions with only two patients (both statistical significance and clinical common sense would be lacking). Such full pursuit of individual ethics to the exclusion of collec-tive ethics seriously conflicts with the conduct of randomised controlled trials. Unbiased and precise comparison of treatments would become impossible, and the development of new treatments would be chaotic and unscientific.
