PDF(Pubmed)
Abstract：
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 a priori 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.
摘要：
这项研究总结了卡尔文·施瓦贝兽医流行病学和预防医学终身成就奖研讨会上的演讲，授予第一作者。作为流行病学家,我们被教导“相关性并不意味着因果关系。\"虽然真实,确定原因是我们进行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观察性研究，目的是确定原因；许多研究包括对混杂变量的控制，解释研究结果时经常使用因果语言。研究原因的框架包括明确要测试的特定假设，变量选择的方法，暴露与结果之间关系的统计估计方法，并将这种关系解释为因果关系。当比较兽医人群的观察性研究与人群的观察性研究时，这些步骤中的每一个的应用基本上不同。与人类文献相比，在兽医文献的观察性研究中，对感兴趣的暴露-结果对的先验识别不太常见。在大多数人群的观察性研究中，先验知识被用来选择混杂变量，而数据驱动的方法是兽医人群的常态。没有明确的暴露结果假设和使用数据驱动的分析方法的后果包括偏颇结果的可能性增加和结果的可复制性差。有必要由研究人员社区讨论当前在兽医人群的观察性研究中研究原因的方法。
