Bayesian perspectives for epidemiological research: I. Foundations and basic methods
Bayesian perspectives for epidemiological research: I. Foundations and basic methods
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DOI：
10.1093/ije/dyi312
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发表时间：
2006-06-01
影响因子：
7.7
通讯作者：
Greenland, Sander
中科院分区：
文献类型：
--
作者：
Greenland, Sander
关于贝叶斯分析的一个误解是，先验分布引入的假设比频率论方法所做的假设更有问题;然而，先验分布中的假设可能比标准频率论模型中隐含的假设更合理。另一个误解是贝叶斯方法计算困难，需要特殊的软件。但完全足够的贝叶斯分析可以进行常见的软件频率分析。在广泛的先验条件下，这些近似值的准确性与软件的频率论准确性一样好，并且对于健康和社会科学中发现的不准确的观察研究来说绰绰有余。进行贝叶斯分析的一种简单方法是通过对先验信息和频率估计进行逆方差（信息）加权平均。更一般的方法是以先验数据或“数据等价物”的形式表示先验分布，然后将其作为新的数据层输入分析。这种形式显示了所采用的先前判决的力度，并可能导致这些判决的缓和。有人认为，先验分布的科学可接受性的一个标准是，它可以表示为先验数据，因此，先验假设的强度可以衡量有多少数据，他们代表。
One misconception (of many) about Bayesian analyses is that prior distributions introduce assumptions that are more questionable than assumptions made by frequentist methods; yet the assumptions in priors can be more reasonable than the assumptions implicit in standard frequentist models. Another misconception is that Bayesian methods are computationally difficult and require special software. But perfectly adequate Bayesian analyses can be carried out with common software for frequentist analysis. Under a wide range of priors, the accuracy of these approximations is just as good as the frequentist accuracy of the software-and more than adequate for the inaccurate observational studies found in health and social sciences. An easy way to do Bayesian analyses is via inverse-variance (information) weighted averaging of the prior with the frequentist estimate. A more general method expresses the prior distributions in the form of prior data or 'data equivalents', which are then entered in the analysis as a new data stratum. That form reveals the strength of the prior judgements being introduced and may lead to tempering of those judgements. It is argued that a criterion for scientific acceptability of a prior distribution is that it be expressible as prior data, so that the strength of prior assumptions can be gauged by how much data they represent.