Suissa S
Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada.
Drug Saf. 1991 Sep-Oct;6(5):381-9. doi: 10.2165/00002018-199106050-00008.
To successfully appraise the significance of epidemiological data on drug risk and safety requires a good understanding of the errors involved in the design and analysis of pharmacoepidemiological studies. A proper comprehension of the repercussions of these errors and of the strengths and limitations of the tools used to measure their magnitude are essential to sound decision making by the regulatory, industry or clinical consumers of these data. In this paper, we examine the role of statistics in managing the quantifiable errors present in pharmacoepidemiological data analysis and interpretation. Some epidemiological principles on the measurement of risk are first introduced. The influences of controllable systematic error and random error on our assessment of epidemiological data are then presented, along with the prevailing statistical principles and measures necessary to control these errors. To illustrate the various issues addressed, published data on the risks of NSAIDs, focusing particularly on upper gastrointestinal bleeding (UGIB), the risks of replacement estrogens for endometrial cancer and the safety of allopurinol for cataracts are used as examples throughout.
要成功评估药物风险与安全性的流行病学数据的重要性,需要充分理解药物流行病学研究设计与分析中涉及的误差。正确理解这些误差的影响以及用于衡量其大小的工具的优缺点,对于监管机构、行业或临床数据使用者做出明智决策至关重要。在本文中,我们探讨统计学在管理药物流行病学数据分析与解释中存在的可量化误差方面的作用。首先介绍一些关于风险测量的流行病学原理。然后阐述可控系统误差和随机误差对我们评估流行病学数据的影响,以及控制这些误差所需的主流统计原理和方法。为说明所讨论的各种问题,文中始终以关于非甾体抗炎药(NSAIDs)风险(尤其关注上消化道出血[UGIB])、替代雌激素对子宫内膜癌的风险以及别嘌醇对白内障安全性的已发表数据为例。