流行病学分析中的陷阱。

G Carlsson
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引用次数: 1

摘要

流行病学家在他们的分析和报告中严重依赖于相对风险。作为一种指数,它是可理解的,直观地吸引人,但可能会给人一种夸张的联想强度的印象,并且不可靠的比较。这可以通过从具有不起眼的相关性水平的正相关表面导出相对风险来显示。相对风险应谨慎处理;应报告潜在的人口风险以及受照射类别和参考类别的相对规模。通过某种多变量技术(另一种标准程序)控制额外变量、混杂因素的努力很容易给人一种虚假的安全感。文献中不时地明确指出,第三个变量或其他变量的测量误差可能导致虚假的独立效应的出现,但这些警告似乎没有得到应有的重视。通过一个模拟实验,结合实际的数值假设,将这一教训带回家。中等程度的误差污染会产生虚假效果。这与抽样误差无关,大样本反而加剧了这种危险。在元研究中,这是一个需要考虑的错误和结果冲突的来源。
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Pitfalls in epidemiological analysis.

Epidemiologists rely heavily on the relative risk in their analyses and presentations. As an index it is intelligible and intuitively appealing but can give an exaggerated impression of the strength of the association and is unreliable for comparisons. This can be shown by deriving relative risks from a normal correlation surface with an unimpressive level of correlation. Relative risks ought to be handled with caution; the underlying population risk and the relative size of exposed and reference categories should be reported. Efforts to control for additional variables, confounders, by some kind of multi-variate technique, another standard procedure, could easily give a false sense of security. From time to time it has been made clear in the literature that errors of measurement in the third variable or in the additional variables could lead to the appearance of false independent effects, but these warnings do not seem to have been heeded nearly as much as they deserve. A simulation experiment is used to bring the lesson home, with realistic numerical assumptions. A moderate degree of error contamination will produce spurious effects. This has nothing to do with sampling errors, large samples rather aggravate this danger. In meta-studies this is a source of error and conflicting results to take account of.

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