Monte Carlo modeling of epidemiological studies

Abstract

Abstract 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.