We propose sequential multiple testing procedures which control the false discover rate (FDR) or the positive false discovery rate (pFDR) under arbitrary dependence between the data streams. This is accomplished by "optimizing" an upper bound on these error metrics for a class of step down sequential testing procedures. Both open-ended and truncated versions of these sequential procedures are given, both being able to control both the type~I multiple testing metric (FDR or pFDR) at specified levels, and the former being able to control both the type I and type II (e.g., FDR and the false nondiscovery rate, FNR). In simulation studies, these procedures provide 45-65% savings in average sample size over their fixed-sample competitors. We illustrate our procedures on drug data from the United Kingdom's Yellow Card Pharmacovigilance Database.

翻译：本文提出了一种序贯多重检验方法，可在数据流间存在任意依赖关系的条件下控制错误发现率（FDR）或阳性错误发现率（pFDR）。该目标通过针对一类序贯逐步下降检验方法优化这些误差度量的上界而实现。我们给出了这些序贯方法的开放式与截断式两种版本：二者均能在指定水平上控制I类多重检验度量（FDR或pFDR），而前者还能同时控制I类和II类误差（例如FDR与错误非发现率FNR）。仿真研究表明，相较于固定样本量方法，本方法可平均节省45-65%的样本量。我们以英国黄卡药物警戒数据库中的药物数据为例展示了本方法的实际应用。