Sargeant Jan M, O'Connor Annette M, Renter David G, Ruple Audrey
Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada.
Department of Large Animal Clinical Sciences, College of Veterinary Medicine, Michigan State University, East Lansing, MI, United States.
Front Vet Sci. 2024 May 21;11:1402981. doi: 10.3389/fvets.2024.1402981. eCollection 2024.
This study summarizes a presentation at the symposium for the Calvin Schwabe Award for Lifetime Achievement in Veterinary Epidemiology and Preventive Medicine, which was awarded to the first author. As epidemiologists, we are taught that "correlation does not imply causation." While true, identifying causes is a key objective for much of the research that we conduct. There is empirical evidence that veterinary epidemiologists are conducting observational research with the intent to identify causes; many studies include control for confounding variables, and causal language is often used when interpreting study results. Frameworks for studying causes include the articulation of specific hypotheses to be tested, approaches for the selection of variables, methods for statistical estimation of the relationship between the exposure and the outcome, and interpretation of that relationship as causal. When comparing observational studies in veterinary populations to those conducted in human populations, the application of each of these steps differs substantially. The identification of exposure-outcome pairs of interest are less common in observational studies in the veterinary literature compared to the human literature, and prior knowledge is used to select confounding variables in most observational studies in human populations, whereas data-driven approaches are the norm in veterinary populations. The consequences of not having a defined exposure-outcome hypotheses of interest and using data-driven analytical approaches include an increased probability of biased results and poor replicability of results. A discussion by the community of researchers on current approaches to studying causes in observational studies in veterinary populations is warranted.
本研究总结了在兽医流行病学与预防医学终身成就卡尔文·施瓦贝奖研讨会上的一次演讲，该奖项授予了第一作者。作为流行病学家，我们深知“相关性并不意味着因果关系”。虽然这是事实，但确定病因是我们开展的许多研究的关键目标。有经验证据表明，兽医流行病学家正在进行旨在确定病因的观察性研究；许多研究包括对混杂变量的控制，并且在解释研究结果时经常使用因果关系的表述。研究病因的框架包括阐述要检验的具体假设、选择变量的方法、对暴露与结果之间关系进行统计估计的方法，以及将这种关系解释为因果关系。当将兽医群体中的观察性研究与人类群体中的观察性研究进行比较时，这些步骤中的每一步的应用都有很大差异。与人类文献相比，在兽医文献的观察性研究中，确定感兴趣的暴露 - 结果对的情况不太常见，并且在大多数人类群体的观察性研究中，先验知识用于选择混杂变量，而在兽医群体中，数据驱动的方法是常态。缺乏明确的感兴趣的暴露 - 结果假设并使用数据驱动的分析方法会导致结果出现偏差的可能性增加以及结果的可重复性差。兽医群体中研究病因的当前方法值得研究人员群体进行讨论。
