Then, you monitor the number of bad outcomes. To apply the procedure, you will need to specify a probability that is good enough to accept ( $p_0$), and a probability that is bad enough to reject ($p_1$), in addition to your usual error rates $\alpha$ and $\beta$. However, for sequential testing, the original paper on the sequential probability ratio test by (Wald, 1945) is very readable and available online. There is plenty of literature on acceptance sampling and process monitoring that also bears on this problem if you need to perform batch sampling. (For example, see II.3 of (Siegmund, 1985)). Your case is usually covered as an example along with the normal distribution. There are many references to cover the theory of sequential testing. If you have very little cost to analysis, then you can just do sequential testing. That is most useful when it is costly to perform interim analyses. It might be that the group sequential testing approach is overkill for your application. Thus I failed to find a guidance how to design the sequential experiment protocol for my set-up: given pre-fixed values for Type I and II errors, the number of stops, I want to find p-value thresholds (or other stopping criteria) for each of the stops.Ĭould anyone provide me with a relevant link? However, as the topic is mostly studied in the medical context, usually a control/test split is assumed. There exist quite extensive literature on sequential testing, R package gsDesign, etc. This idea is inspired by O'Brien
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