Bayesian Bias Modelling for analysis of observational data

观察性研究是医学、社会科学和经济学实证研究的重要工具。 但主要的困难是，研究结果往往受到偏见的困扰，如混淆和测量误差。 这个研究项目将调查一个新兴的数据分析技术领域，称为贝叶斯偏差模型。 偏倚建模扩展了传统的参数模型，以纳入额外的参数，这些参数可以捕获有关偏倚的假设。 由于观察性研究不包含关于偏倚大小的信息，这意味着所得模型是不可识别的。为了分析数据，研究者以偏倚参数的先验分布的形式提供用户输入。贝叶斯定理然后给出了一个原则性的合成数据与先验信念的偏见。 因此，贝叶斯偏差建模技术能够更好地量化观察性研究中的总不确定性，而不是由于随机误差而导致的通常的不确定性。

Observational studies are important tools for empirical research in medicine, the social sciences and economics. But the chief difficulty is that study findings are often plagued with biases, such as confounding and measurement error. This research program will investigate a burgeoning area of data analytic techniques called Bayesian bias modelling. Bias modelling extends conventional parametric models to incorporate additional parameters which capture assumptions about bias. Because the observational study contains no information about the magnitude of bias, it means that the resulting model is nonidentifiable. To analyze the data, the investigator supplies user inputs in the form of prior distributions for bias parameters. Bayes theorem then gives a principled synthesis of the data with prior beliefs about bias. Thus Bayesian bias modelling techniques are better able to quantify total uncertainty in observational studies rather than the usual uncertainty due to random error.