Sensitivity analyses for sparse-data problems-using weakly informative bayesian priors.
Sensitivity analyses for sparse-data problems-using weakly informative bayesian priors.
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DOI：
10.1097/ede.0b013e318280db1d
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发表时间：
2013-03
期刊：
影响因子：
--
通讯作者：
Cole SR
中科院分区：
文献类型：
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作者：
Hamra GB;MacLehose RF;Cole SR
稀疏数据问题是常见的，并且需要方法来评估基于稀疏数据的参数估计的灵敏度。我们提出了一个贝叶斯方法，使用弱信息先验量化参数的敏感性稀疏数据。弱信息先验是基于积累的证据，使用疾病关联的相对措施的关系的预期幅度。我们举例说明了使用弱信息先验的一个例子，终身饮酒和头颈癌的关联。当数据稀疏且观测信息较弱时，弱信息先验将使参数估计值向先验均值收缩。此外，该例子表明，当数据不稀疏，观察到的信息不弱，弱信息先验是没有影响的。马尔可夫链蒙特卡罗模拟的实施进展，使这种敏感性分析很容易获得的实践流行病学家。
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.