Multiple-bias modelling for analysis of observational data
Multiple-bias modelling for analysis of observational data
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DOI：
10.1111/j.1467-985x.2004.00349.x
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发表时间：
2005-01-01
影响因子：
2
通讯作者：
Greenland, S
中科院分区：
文献类型：
--
作者：
Greenland, S
传统的分析结果除了随机误差外，不能反映任何不确定性的来源，因此读者必须依靠关于可能偏差影响的非正式判断。当标准误差很小时，这些判断往往不能充分捕捉不确定性的来源及其相互作用。多偏差模型提供了备选方案，使人们能够系统地整合主要的不确定性来源，从而为研究规划和政策分析提供更好的输入。通常，模型中的偏差参数无法由分析数据识别，因此结果完全依赖于这些参数的先验。因此，贝叶斯分析是很自然的，但在风险评估和流行病学文献中出现了几种基于敏感性分析的替代方法。在某些情况下，这些方法近似于贝叶斯分析，并且可以修改得更好。通过对住宅磁场暴露与儿童白血病的病例对照研究的汇总分析，可以说明这些观点，这些研究强调了20世纪90年代初以后进行的传统研究的价值正在下降。有人认为，多偏差建模应该成为任何被委托进行观测数据分析的人的核心培训的一部分，当随机误差不是不确定性的唯一重要来源时（如在荟萃分析和汇集分析中），它应该成为标准程序。
Conventional analytic results do not reflect any source of uncertainty other than random error, and as a result readers must rely on informal judgments regarding the effect of possible biases. When standard errors are small these judgments often fail to capture sources of uncertainty and their interactions adequately. Multiple-bias models provide alternatives that allow one systematically to integrate major sources of uncertainty, and thus to provide better input to research planning and policy analysis. Typically, the bias parameters in the model are not identified by the analysis data and so the results depend completely on priors for those parameters. A Bayesian analysis is then natural, but several alternatives based on sensitivity analysis have appeared in the risk assessment and epidemiologic literature. Under some circumstances these methods approximate a Bayesian analysis and can be modified to do so even better. These points are illustrated with a pooled analysis of case-control studies of residential magnetic field exposure and childhood leukaemia, which highlights the diminishing value of conventional studies conducted after the early 1990s. It is argued that multiple-bias modelling should become part of the core training of anyone who will be entrusted with the analysis of observational data, and should become standard procedure when random error is not the only important source of uncertainty (as in meta-analysis and pooled analysis).