Beware of registries for their biases.

Hasan Yazici
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Abstract

Patient registries are very popular. On the other hand, scientific data collections in registries are commonly observational and retrospective and, in many instances, are prone to biases. Same thing is true of administrative data bases. The selection of the control group(s) is probably the Achilles heel of scientific data collection in observational studies, and there are historical examples of how a properly chosen control group can help or its absence deceive us. Somewhat more recently recognized biases are the wandering comparisons of risk, confounding by disease severity, channeling bias, depletion of the susceptible, and the immortal time bias. The last bias can especially be deceiving and give us false hopes of new remedies. A particularly important selection bias we have come across is what we call the "mortality bias." This is where the mortality in a mother population lessens the mortality in the registry that stems from this mother population simply because deaths in the former cannot be represented in the latter.

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要小心注册表的偏见。
病人登记非常流行。另一方面,注册表中的科学数据收集通常是观察性和回顾性的,在许多情况下,容易产生偏差。管理数据库也是如此。在观察性研究中,对照组的选择可能是科学数据收集的阿喀琉斯之踵,历史上有一些例子表明,正确选择的对照组是如何帮助我们的,而没有对照组则会欺骗我们。最近人们认识到的偏差包括风险的随机比较、疾病严重程度的混淆、疏导偏差、易感人群的减少以及不朽的时间偏差。最后一种偏见尤其具有欺骗性,让我们对新疗法抱有错误的希望。我们遇到的一个特别重要的选择偏差就是我们所说的“死亡率偏差”。在这种情况下,母亲人口的死亡率减少了来自该母亲人口的登记死亡率,因为前者的死亡无法在后者中表示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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