流行病学数据在癌症风险评估中的应用。

S M Brown
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引用次数: 0

摘要

流行病学数据对于人类癌症风险的定量估计是非常重要的,可以用(a)归因风险或(b)相对风险来表示。虽然病例对照研究的成本/效益比较低,但队列研究更有可能产生与暴露量有关的风险数据,并允许精确计算风险。然而,队列研究时间长、费用高、管理困难。磨损和由此产生的不具代表性的成分可能是主要问题。为了准确计算风险,应该跟踪队列直至灭绝,这必须考虑到延长的潜伏期、竞争风险以及风险随时间(或年龄)增加的可能性。鉴于流行病学和病毒学方法的某些局限性,需要对病毒和致癌基因理论进行批判性的重新评估。使用动物数据来评估人类癌症风险需要进一步调查支持两种外推的假设和事实:从高剂量到低剂量的致癌物质暴露,以及跨物种外推(即从实验动物到人类)。这两种方法的大部分都基于值得进一步关注的可检验假设。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The use of epidemiologic data in the assessment of cancer risk.

Epidemiologic data can be extremely important in arriving at quantitative estimates of human cancer risk, expressed either as (a) attributable risk, or (b) relative risk. While case-control studies have a low cost/benefit ratio, cohort studies are more likely to yield data relating risk to amount of exposure, and to allow precise calculation of risk. However, cohort studies are long, expensive, and difficult to administer. Attrition and resultant unrepresentative composition can be major problems. Cohorts should be followed to extinction to permit accurate calculation of risk, which must take into account prolonged latency periods, competing risks, and the likelihood of increasing risk with time (or age). In view of certain limitations in epidemiologic and virologic methods, critical re-evaluation of the viral and oncogene theories are called for. The use of animal data for assessing human cancer risk requires further investigation of the assumptions and facts supporting two kinds of extrapolation: That from high to low dose carcinogen exposure, and cross-species extrapolation (i.e., from experimental animals to man). Large pieces of these two approaches rest on testable hypotheses which deserve further attention.

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