Davies R, London C, Lascelles B, Conzemius M
College of Veterinary Medicine, University of Minnesota, St. Paul, MN, USA.
Cummings School of Veterinary Medicine, Tufts University, North Grafton, MA, USA.
BMC Vet Res. 2017 Aug 16;13(1):242. doi: 10.1186/s12917-017-1153-x.
Veterinary clinical trials generate data that advance the transfer of knowledge from clinical research to clinical practice in human and veterinary settings. The translational success of non-regulated and regulated veterinary clinical studies is dependent upon the reliability and reproducibility of the data generated. Clinician-scientists that conduct veterinary clinical studies would benefit from a commitment to research quality assurance and best practices throughout all non-regulated and regulated research environments. Good Clinical Practice (GCP) guidance documents from the FDA provides principles and procedures designed to safeguard data integrity, reliability and reproducibility. While these documents maybe excessive for clinical studies not intended for regulatory oversight it is important to remember that research builds on research. Thus, the quality and accuracy of all data and inference generated throughout the research enterprise remains vulnerable to the impact of potentially unreliable data generated by the lowest performing contributors. The purpose of this first of a series of statement papers is to outline and reference specific quality control and quality assurance procedures that should, at least in part, be incorporated into all veterinary clinical studies.
兽医临床试验所产生的数据推动了知识从临床研究向人类和兽医临床实践的转化。非规范化和规范化兽医临床研究的转化成功取决于所产生数据的可靠性和可重复性。开展兽医临床研究的临床科学家将受益于在所有非规范化和规范化研究环境中对研究质量保证和最佳实践的承诺。美国食品药品监督管理局（FDA）的《药物临床试验质量管理规范》（GCP）指导文件提供了旨在保障数据完整性、可靠性和可重复性的原则和程序。虽然这些文件对于不打算接受监管监督的临床研究可能过于繁琐，但重要的是要记住研究是建立在研究基础之上的。因此，整个研究企业所产生的所有数据和推断的质量和准确性仍然容易受到表现最差的参与者所产生的潜在不可靠数据的影响。这一系列声明文件中的第一篇的目的是概述并引用特定的质量控制和质量保证程序，这些程序至少应部分纳入所有兽医临床研究中。
