Department of Statistics, University of British Columbia, 333-6356 Agricultural Road, Vancouver, B.C., V6T 1Z2, Canada.
Int J Environ Res Public Health. 2010 Apr;7(4):1520-39. doi: 10.3390/ijerph7041520. Epub 2010 Apr 1.
Typical statistical analysis of epidemiologic data captures uncertainty due to random sampling variation, but ignores more systematic sources of variation such as selection bias, measurement error, and unobserved confounding. Such sources are often only mentioned via qualitative caveats, perhaps under the heading of 'study limitations.' Recently, however, there has been considerable interest and advancement in probabilistic methodologies for more integrated statistical analysis. Such techniques hold the promise of replacing a confidence interval reflecting only random sampling variation with an interval reflecting all, or at least more, sources of uncertainty. We survey and appraise the recent literature in this area, giving some prominence to the use of Bayesian statistical methodology.
流行病学数据的典型统计分析捕捉到了由于随机抽样变化引起的不确定性，但忽略了更系统的变化来源，如选择偏差、测量误差和未观察到的混杂。这些来源通常仅通过定性警告提及，也许在“研究局限性”标题下。然而，最近人们对更综合的统计分析的概率方法产生了浓厚的兴趣并取得了进展。这些技术有望用反映所有（或至少更多）不确定性来源的区间取代仅反映随机抽样变化的置信区间。我们调查和评估了该领域的最新文献，特别强调了贝叶斯统计方法的使用。
