School of Psychological Science, University of Bristol, Bristol BS8 1TU, United Kingdom.
MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, United Kingdom.
Cold Spring Harb Perspect Med. 2021 Aug 2;11(8):a040659. doi: 10.1101/cshperspect.a040659.
Much research effort is invested in attempting to determine causal influences on disease onset and progression to inform prevention and treatment efforts. However, this is often dependent on observational data that are prone to well-known limitations, particularly residual confounding and reverse causality. Several statistical methods have been developed to support stronger causal inference. However, a complementary approach is to use design-based methods for causal inference, which acknowledge sources of bias and attempt to mitigate these through the design of the study rather than solely through statistical adjustment. Genetically informed methods provide a novel and potentially powerful extension to this approach, accounting by design for unobserved genetic and environmental confounding. No single approach will be absent from bias. Instead, we should seek and combine evidence from multiple methodologies that each bring different (and ideally uncorrelated) sources of bias. If the results of these different methodologies align-or triangulate-then we can be more confident in our causal inference. To be truly effective, this should ideally be done prospectively, with the sources of evidence specified in advance, to protect against one final source of bias-our own cognitions, expectations, and fondly held beliefs.
大量的研究工作都投入到了试图确定疾病发病和进展的因果影响，以提供预防和治疗的依据。然而，这通常依赖于容易受到众所周知的局限性影响的观察性数据，特别是残余混杂和反向因果关系。已经开发了几种统计方法来支持更强的因果推断。然而，一种补充方法是使用基于设计的方法进行因果推断，这些方法承认偏倚的来源，并试图通过研究设计来减轻这些偏倚，而不仅仅是通过统计调整。遗传信息方法为这种方法提供了一个新颖而潜在强大的扩展，通过设计来解释未观察到的遗传和环境混杂。没有一种方法可以完全避免偏差。相反，我们应该从多种方法学中寻找和综合证据，这些方法学各自带来不同的（理想情况下是不相关的）偏倚来源。如果这些不同方法学的结果一致——或者说是三角验证——那么我们就可以更有信心进行因果推断。为了真正有效，这最好是前瞻性地进行，预先确定证据的来源，以防止最后一个偏倚来源——我们自己的认知、期望和固有的信念。
