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首页> 外文期刊>Annals of epidemiology >Bias formulas for external adjustment and sensitivity analysis of unmeasured confounders.
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Bias formulas for external adjustment and sensitivity analysis of unmeasured confounders.

机译：外部调整的偏见公式和未测混杂因素的敏感性分析。

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摘要

PURPOSE: Uncontrolled confounders are an important source of bias in epidemiologic studies. The authors review and derive a set of parallel simple formulas for bias factors in the risk difference, risk ratio, and odds ratio from studies with an unmeasured polytomous confounder and a dichotomous exposure and outcome. METHODS: The authors show how the bias formulas are related to and are sometimes simpler than earlier formulas. The article contains three examples, including a Monte Carlo sensitivity analysis of a preadjusted or conditional estimate. RESULTS: All the bias expressions can be given parallel formulations as the difference or ratio of (i) the sum across confounder strata of each exposure-stratified confounder-outcome effect measure multiplied by the confounder prevalences among the exposed and (ii) the sum across confounder strata of the same effect measure multiplied by the confounder prevalences among the unexposed. The basic formulas can be applied to scenarios with a polytomous confounder, exposure, or outcome. CONCLUSIONS: In addition to aiding design and analysis strategies for confounder control, the bias formulas provide a link between classical standardization decompositions of demography and classical bias formulas of epidemiology. They are also useful in constructing general programs for sensitivity analysis and more elaborate probabilistic risk analyses.
机译：目的：不受控制的混杂因素是流行病学研究中偏见的重要来源。作者审查并得出了一组平行的简单公式，用于对风险差异，风险比和比值比中的偏倚因素进行评估，这些研究来自于未测的多变量混杂因素以及二分暴露和结果。方法：作者展示了偏差公式与早期公式之间的关系，有时甚至更简单。本文包含三个示例，包括对预先调整的或有条件的估计值进行的蒙特卡洛敏感性分析。结果：所有偏差表达式都可以采用平行公式表示，即（i）每个暴露分层的混杂结果结果量度的混杂层总和乘以暴露之间的混杂发生率和（ii）总数之差或比率。效果相同的混杂因素层乘以未暴露人群中混杂因素的患病率。基本公式可以应用于具有多因素混杂，暴露或结果的情况。结论：除了辅助设计和分析策略以控制混杂因素外，偏差公式还提供了人口统计学的经典标准化分解与流行病学经典偏差公式之间的联系。它们在构建用于敏感性分析和更详细的概率风险分析的通用程序中也很有用。

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