Overadjustment bias and unnecessary adjustment in epidemiologic studies.
Overadjustment bias and unnecessary adjustment in epidemiologic studies.
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DOI：
10.1097/ede.0b013e3181a819a1
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发表时间：
2009-07
期刊：
影响因子：
--
通讯作者：
Platt RW
中科院分区：
文献类型：
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作者：
Schisterman EF;Cole SR;Platt RW
过度调整的定义并不一致。这个术语是用来描述对变量的控制(例如，通过回归调整、分层或限制)，这种控制要么增加净偏差，要么降低精度而不影响偏差。我们将过度调整偏差定义为对从暴露到结果的因果路径上的中间变量(或中间变量的递减替代)的控制。我们将不必要的调整定义为对一个变量的控制，该变量不影响暴露与结果之间的因果关系的偏差，但可能影响其精确度。我们使用因果图和一个实证例子(母亲吸烟对新生儿死亡率的影响)来说明和澄清过度调整偏差的定义，并区分过度调整偏差和不必要调整。通过模拟，我们量化了与过度调整相关的偏差量。此外，我们还表明，这种偏差是基于与混淆或选择偏差不同的因果结构。超调偏差不是有限的样本偏差，而由于控制不必要的变量而导致的低效则是样本大小的函数。
Overadjustment is defined inconsistently. This term is meant to describe control (eg, by regression adjustment, stratification, or restriction) for a variable that either increases net bias or decreases precision without affecting bias. We define overadjustment bias as control for an intermediate variable (or a descending proxy for an intermediate variable) on a causal path from exposure to outcome. We define unnecessary adjustment as control for a variable that does not affect bias of the causal relation between exposure and outcome but may affect its precision. We use causal diagrams and an empirical example (the effect of maternal smoking on neonatal mortality) to illustrate and clarify the definition of overadjustment bias, and to distinguish overadjustment bias from unnecessary adjustment. Using simulations, we quantify the amount of bias associated with overadjustment. Moreover, we show that this bias is based on a different causal structure from confounding or selection biases. Overadjustment bias is not a finite sample bias, while inefficiencies due to control for unnecessary variables are a function of sample size.