Department of Epidemiology, UNC Chapel Hill, Chapel Hill, NC 27599-7435, USA.
Epidemiology. 2013 Mar;24(2):233-9. doi: 10.1097/EDE.0b013e318280db1d.
Sparse-data problems are common, and approaches are needed to evaluate the sensitivity of parameter estimates based on sparse data. We propose a Bayesian approach that uses weakly informative priors to quantify sensitivity of parameters to sparse data. The weakly informative prior is based on accumulated evidence regarding the expected magnitude of relationships using relative measures of disease association. We illustrate the use of weakly informative priors with an example of the association of lifetime alcohol consumption and head and neck cancer. When data are sparse and the observed information is weak, a weakly informative prior will shrink parameter estimates toward the prior mean. Additionally, the example shows that when data are not sparse and the observed information is not weak, a weakly informative prior is not influential. Advancements in implementation of Markov Chain Monte Carlo simulation make this sensitivity analysis easily accessible to the practicing epidemiologist.
稀疏数据问题很常见，需要有方法来评估基于稀疏数据的参数估计的敏感性。我们提出了一种贝叶斯方法，该方法使用弱信息先验来量化参数对稀疏数据的敏感性。弱信息先验基于使用疾病关联的相对度量来累积关于关系预期幅度的证据。我们通过一个终生饮酒与头颈部癌症关联的例子来说明弱信息先验的使用。当数据稀疏且观察到的信息较弱时，弱信息先验会将参数估计值向先验平均值收缩。此外，该示例还表明，当数据不稀疏且观察到的信息不弱时，弱信息先验不会产生影响。马尔可夫链蒙特卡罗模拟的实施进展使得这种敏感性分析易于为实践中的流行病学家所使用。
