A conditional maximized sequential probability ratio test for pharmacovigilance
A conditional maximized sequential probability ratio test for pharmacovigilance
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DOI：
10.1002/sim.3780
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发表时间：
2010-01-30
影响因子：
2
通讯作者：
Kulldorff, Martin
中科院分区：
文献类型：
--
作者：
Li, Lingling;Kulldorff, Martin
上市后监测对药物和疫苗安全性的重要性是公认的，因为在批准前的临床试验中可能不会发现罕见但严重的不良事件。在这种监视中，顺序测试是可取的，以便尽快发现潜在的问题。各种序列概率比检验（SPRT）已应用于近实时的疫苗和药物安全监测，包括Wald的经典SPRT（单一替代）和基于泊松的最大化SPRT （MaxSPRT）和复合替代。这些方法要求在零假设下的期望事件数是时间t的函数。在实践中，期望计数通常是根据历史数据估计的。当缺乏来自历史数据的大量样本时，由于预期事件数量估计的差异，sprt是有偏差的。我们提出了一个条件最大化序列概率比检验（CMaxSPRT），它调整了期望计数中的不确定性。我们的测试结合了历史数据和监测人群的随机性和可变性。对CMaxSPRT在不同场景下的统计能力进行了评价。版权所有John Wiley & Sons， Ltd. 2009
The importance of post-marketing surveillance for drug and vaccine safety is well recognized as rare but serious adverse events may not be detected in pre-approval clinical trials. In such surveillance, a sequential test is preferable, in order to detect potential problems as soon as possible. Various sequential probability ratio tests (SPRT) have been applied in near real-time vaccine and drug safety surveillance, including Wald's classical SPRT with a single alternative and the Poisson-based maximized SPRT (MaxSPRT) with a composite alternative. These methods require that the expected number of events under the null hypothesis is known as a function of time t. In practice, the expected counts are usually estimated from historical data. When a large sample size from the historical data is lacking, the SPRTs are biased due to the variance in the estimate of the expected number of events. We present a conditional maximized sequential probability ratio test (CMaxSPRT), which adjusts for the uncertainty in the expected counts. Our test incorporates the randomness and variability from both the historical data and the surveillance population. Evaluations of the statistical power for CMaxSPRT are presented under different scenarios. Copyright (C) 2009 John Wiley & Sons, Ltd.