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首页> 外文期刊>Pharmacoepidemiology and drug safety >Signal detection on spontaneous reports of adverse events following immunisation: A comparison of the performance of a disproportionality-based algorithm and a time-to-onset-based algorithm
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Signal detection on spontaneous reports of adverse events following immunisation: A comparison of the performance of a disproportionality-based algorithm and a time-to-onset-based algorithm

机译：免疫后不良事件自发报告的信号检测：基于不成比例的算法和基于发作时间的算法的性能比较

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Purpose: Disproportionality methods measure how unexpected the observed number of adverse events is. Time-to-onset (TTO) methods measure how unexpected the TTO distribution of a vaccine-event pair is compared with what is expected from other vaccines and events. Our purpose is to compare the performance associated with each method. Methods: For the disproportionality algorithms, we defined 336 combinations of stratification factors (sex, age, region and year) and threshold values of the multi-item gamma Poisson shrinker (MGPS). For the TTO algorithms, we defined 18 combinations of significance level and time windows. We used spontaneous reports of adverse events recorded for eight vaccines. The vaccine product labels were used as proxies for true safety signals. Algorithms were ranked according to their positive predictive value (PPV) for each vaccine separately; amedian rank was attributed to each algorithm across vaccines. Results: The algorithm with the highest median rank was based on TTO with a significance level of 0.01 and a time window of 60days after immunisation. It had an overall PPV 2.5 times higher than for the highest-ranked MGPS algorithm, 16th rank overall, which was fully stratified and had a threshold value of 0.8. A TTO algorithm with roughly the same sensitivity as the highest-ranked MGPS had better specificity but longer time-to-detection. Conclusions: Within the scope of this study, the majority of the TTO algorithms presented a higher PPV than for any MGPS algorithm. Considering the complementarity of TTO and disproportionality methods, a signal detection strategy combining them merits further investigation.
机译：目的：不成比例的方法测量观察到的不良事件数量有多意外。发病时间（TTO）方法可衡量将疫苗事件对的TTO分布与其他疫苗和事件所预期的出乎意料的相比如何。我们的目的是比较每种方法的性能。方法：对于不成比例的算法，我们定义了336个分层因子（性别，年龄，区域和年份）和多项目伽马泊松收缩器（MGPS）阈值的组合。对于TTO算法，我们定义了重要性级别和时间窗口的18种组合。我们使用关于八种疫苗的不良事件的自发报告。疫苗产品标签用作真实安全信号的代理。分别根据每种疫苗的阳性预测值（PPV）对算法进行排名;每种疫苗的每种算法均得出中间等级。结果：中位数排名最高的算法是基于TTO的，显着性水平为0.01，免疫后60天的时间窗。它的总体PPV高于排名最高的MGPS算法的2.5倍，整体排名第16位，该算法已完全分层，阈值为0.8。灵敏度与排名最高的MGPS大致相同的TTO算法具有更好的特异性，但检测时间更长。结论：在这项研究的范围内，大多数TTO算法提出的PPV高于任何MGPS算法。考虑到TTO和不成比例方法的互补性，结合它们的信号检测策略值得进一步研究。

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