Observational research provides valuable opportunities to advance oral health science but is limited by vulnerabilities to systematic bias, including unmeasured confounding, errors in variable measurement, or bias in the creation of study populations and/or analytic samples. The potential influence of systematic biases on observed results is often only briefly mentioned among the discussion of limitations of a given study, despite existing methods that support detailed assessments of their potential effects. Quantitative bias analysis is a set of methodological techniques that, when applied to observational data, can provide important context to aid in the interpretation and integration of observational research findings into the broader body of oral health research. Specifically, these methods were developed to provide quantitative estimates of the potential magnitude and direction of the influence of systematic biases on observed results. We aim to encourage and facilitate the broad adoption of quantitative bias analyses into observational oral health research. To this end, we provide an overview of quantitative bias analysis techniques, including a step-by-step implementation guide. We also provide a detailed appendix that guides readers through an applied example using real data obtained from a prospective observational cohort study of preconception periodontitis in relation to time to pregnancy. Quantitative bias analysis methods are available to all investigators. When appropriately applied to observational studies, findings from such studies can have a greater impact in the broader research context.

中文翻译：

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系统偏差的定量评估：研究人员指南。

观察性研究为推进口腔健康科学提供了宝贵的机会，但受到系统偏差的影响，包括无法测量的混杂、变量测量的错误或研究人群和/或分析样本创建中的偏差。尽管现有方法支持对其潜在影响进行详细评估，但在讨论特定研究的局限性时，通常仅简要提及系统偏差对观察结果的潜在影响。定量偏差分析是一套方法论技术，当应用于观察数据时，可以提供重要的背景来帮助解释观察研究结果并将其整合到更广泛的口腔健康研究中。具体来说，开发这些方法是为了定量估计系统偏差对观测结果影响的潜在大小和方向。我们的目标是鼓励和促进在观察性口腔健康研究中广泛采用定量偏差分析。为此，我们提供了定量偏差分析技术的概述，包括分步实施指南。我们还提供了详细的附录，指导读者通过一个应用示例，使用从孕前牙周炎与怀孕时间相关的前瞻性观察队列研究中获得的真实数据。所有研究人员都可以使用定量偏差分析方法。当适当应用于观察性研究时，此类研究的结果可以在更广泛的研究背景中产生更大的影响。