Monte Carlo modeling of epidemiological studies
Monte Carlo modeling of epidemiological studies
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DOI：
10.1080/10807039609383656
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发表时间：
1996-12-01
影响因子：
4.3
通讯作者：
Wilson, R
中科院分区：
文献类型：
--
作者：
Shlyakhter, A;Mirny, L;Wilson, R
随着流行病学家寻找越来越小的影响，他们研究中的统计不确定性可能会被偏见和系统不确定性所掩盖。我们在这里建议，蒙特卡罗技术是非常有用的估计这些偏差和不确定性，也许完全避免它们。我们通过两个简单的蒙特卡罗模拟来说明这一点。首先，我们展示了如何经常假阳性的结果，有时假阴性的结果，可以导致33个不同的错误分类的暴露状态。其次，我们展示了一个偏见，我们称之为“分箱偏见”，可以导致如果调查员选择箱边界后，他已经看到的数据。我们展示了如何通过增加不确定性界限来允许这种偏差。这将使结果的呈现方式与物理科学中的呈现方式相同，在物理科学中，系统误差的定量估计通常包括在最终结果中。最后，我们建议如何在研究之前和研究期间进行类似的Monte Carlo模拟，以完全避免偏差。
As epidemiologists search for smaller and smaller effects, the statistical uncertainty in their studies can be dwarfed by biases and systematic uncertainty. We here suggest that Monte Carlo techniques are very useful to estimate some of these biases and uncertainties, and perhaps to avoid them entirely. We illustrate this by two simple Monte Carlo simulations. First, we show how often false positive findings, and sometimes false negative findings, can result from 33 differential misclassification of the exposure status. Secondly, we show how a bias, that we call ''the binning bias,'' can be caused if the investigator chooses bin boundaries after he has seen the data. We show how an allowance might be made for such a bias by increasing the uncertainty bounds. This would put the presentation of the results on a par with the presentation in physical sciences where a quantitative estimate of systematic errors is routinely included with the final result. Finally, we suggest how similar Monte Carlo simulations carried out before and during the study can be used to avoid the biases entirely.