Observational studies examining causal effects rely on unverifiable assumptions, the violation of which can induce multiple biases. Quantitative bias analysis (QBA) methods examine the sensitivity of findings to such violations, generally, by producing estimates under alternative assumptions, incorporating external information. Although substantial guidance exists for implementing QBA, there is limited guidance on how to systematically determine the assumptions underlying a primary causal analysis and the potential violations that should guide bias analysis. Consequently, many assumptions remain implicit, leading to selective and therefore misleading QBA. To address this gap, we propose a roadmap for systematically identifying and analysing multiple biases. Briefly, this consists of (1) articulating the assumptions underlying the primary analysis through specification and emulation of the ideal trial that defines the causal estimand and depicting these assumptions using a causal diagram; (2) extending the diagram to depict alternative assumptions under which biases may arise; (3) obtaining a single estimate that simultaneously corrects for all potential biases. We illustrate the roadmap using an investigation of the effect of breastfeeding on risk of childhood asthma, and through simulations illustrate the need for analysing multiple biases jointly rather than one at a time.

翻译：观察性研究在检验因果效应时依赖于无法验证的假设，这些假设的违反可能引发多重偏误。定量偏误分析（QBA）方法通常通过结合外部信息、在替代假设下生成估计值，来考察研究结果对此类违反的敏感性。尽管实施QBA已有大量指导，但关于如何系统性地确定基础因果分析所依赖的假设以及应指导偏误分析的潜在违反，现有指导有限。因此，许多假设仍处于隐含状态，导致选择性乃至误导性的QBA。为弥补这一不足，我们提出了一套系统识别与分析多重偏误的路线图。简而言之，该路线图包括：（1）通过明确界定因果估计量的理想试验的规范与模拟，阐明基础分析所依据的假设，并利用因果图描绘这些假设；（2）扩展该因果图以描绘可能产生偏误的替代假设；（3）获得一个能同时校正所有潜在偏误的单一估计值。我们以母乳喂养对儿童哮喘风险影响的研究为例演示该路线图，并通过模拟说明需要联合分析多重偏误而非逐一分析的必要性。