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首页> 外文期刊>The Journal of Clinical Pharmacology: Official Journal of the American College of Clinical Pharmacology >Systematic investigation of time windows for adverse event data mining for recently approved drugs.
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Systematic investigation of time windows for adverse event data mining for recently approved drugs.

机译：对最近批准的药物进行不良事件数据挖掘的时间窗的系统研究。

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摘要

The optimum timing of drug safety data mining for a new drug is uncertain. The objective of this study was to compare cumulative data mining versus mining with sliding time windows. Adverse Event Reporting System data (2001-2005) were studied for 27 drugs. A literature database was used to evaluate signals of disproportionate reporting (SDRs) from an urn model data-mining algorithm. Data mining was applied cumulatively and with sliding time windows from 1 to 4 years in width. Time from SDR generation to the appearance of a publication describing the corresponding adverse event was calculated. Cumulative data mining and 1- to 2-year sliding windows produced the most SDRs for recently approved drugs. In the first postmarketing year, data mining produced SDRs an average of 800 days in advance of publications regarding the corresponding drug-event combination. However, this timing advantage reduced to zero by year 4. The optimum window width for sliding windows should increase with time on the market. Data mining may be most useful for early signal detection during the first 3 years of a drug's postmarketing life. Beyond that, it may be most useful for supporting or weakening hypotheses.
机译：新药药物安全性数据挖掘的最佳时机尚不确定。这项研究的目的是比较累积数据挖掘与滑动时间窗挖掘。研究了27种药物的不良事件报告系统数据（2001-2005年）。文献数据库用于评估来自n模型数据挖掘算法的不成比例报告（SDR）信号。数据挖掘是累积应用的，滑动时间窗口的宽度为1至4年。计算从特别提款权产生到描述相应不良事件的出版物出现的时间。累积数据挖掘和1至2年的滑动窗口为最近批准的药物提供了最多的SDR。在上市后的第一年，数据挖掘平均在发布有关相应药物事件组合的SDR之前800天。但是，这种时序优势到第四年降低为零。滑动窗口的最佳窗口宽度应随时间在市场上增加。在药物上市后的前三年，数据挖掘对于早期信号检测可能最有用。除此之外，它对于支持或削弱假设最有用。

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