Bias Analysis Gone Bad.
Bias Analysis Gone Bad.
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偏差分析变坏了。
DOI：
10.1093/aje/kwab072
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发表时间：
2021
影响因子：
5
通讯作者：
MacLehose,RichardF
中科院分区：
文献类型：
--
作者：
Lash,TimothyL;Ahern,ThomasP;Collin,LindsayJ;Fox,MatthewP;MacLehose,RichardF
定量偏差分析包括用于估计影响流行病学研究的系统误差的方向、幅度和不确定性的工具。尽管有方法和工具以及良好实践指导，但很少有流行病学研究报告纳入偏差影响的定量估计。对偏见分析缺乏熟悉就可能导致误用，这通常是无意的，但偶尔也可能包括故意误导。我们确定了 3 个次优偏差分析示例，每个常见偏差都有一个。对于每一项，我们都描述了原始研究及其偏差分析，将偏差分析与良好实践进行比较，并描述了如何改进偏差分析和研究结果。我们没有断言原作者进行次优偏差分析的动机。示例中常见的缺点是缺乏明确的偏差模型、计算示例和计算代码；分配给偏差模型参数的值选择不当；并且很少努力去理解与偏差相关的不确定性范围。在偏见分析变得更加普遍之前，社区对偏见分析的呈现、解释和解释的期望将保持不稳定。关注良好实践可以提高质量、避免错误并阻止操纵。
Quantitative bias analysis comprises the tools used to estimate the direction, magnitude, and uncertainty from systematic errors affecting epidemiologic research. Despite the availability of methods and tools, and guidance for good practices, few reports of epidemiologic research incorporate quantitative estimates of bias impacts. The lack of familiarity with bias analysis allows for the possibility of misuse, which is likely most often unintentional but could occasionally include intentional efforts to mislead. We identified 3 examples of suboptimal bias analysis, one for each common bias. For each, we describe the original research and its bias analysis, compare the bias analysis with good practices, and describe how the bias analysis and research findings might have been improved. We assert no motive to the suboptimal bias analysis by the original authors. Common shortcomings in the examples were lack of a clear bias model, computed example, and computing code; poor selection of the values assigned to the bias model’s parameters; and little effort to understand the range of uncertainty associated with the bias. Until bias analysis becomes more common, community expectations for the presentation, explanation, and interpretation of bias analyses will remain unstable. Attention to good practices should improve quality, avoid errors, and discourage manipulation.