Research Statistics, Clinical Research, Pfizer Cambridge, United Kingdom.
Pharmacol Res Perspect. 2015 Aug;3(4):e00162. doi: 10.1002/prp2.162. Epub 2015 Jul 24.
Numerous articles in Nature, Science, Pharmacology Research and Perspectives, and other biomedical research journals over the past decade have highlighted that research is plagued by findings that are not reliable and cannot be reproduced. Poor experiments can occur, in part, as a consequence of inadequate statistical thinking in the experimental design, conduct and analysis. As it is not feasible for statisticians to be involved in every preclinical experiment many of the same journals have published guidelines on good statistical practice. Here, we outline a tool that addresses the root causes of irreproducibility in preclinical research in the pharmaceutical industry. The Assay Capability Tool uses 13 questions to guide scientists and statisticians during the development of in vitro and in vivo assays. It promotes the absolutely essential experimental design and analysis strategies and documents the strengths, weaknesses, and precision of an assay. However, what differentiates it from other proposed solutions is the emphasis on how the resulting data will be used. An assay can be assigned a low, medium, or high rating to indicate the level of confidence that can be afforded when making important decisions using data from that assay. This provides transparency on the appropriate interpretation of the assay's results in the light of its current capability. We suggest that following a well-defined process during assay development and use such as that laid out within the Assay Capability Tool means that whatever the results, positive or negative, a researcher can have confidence to make decisions upon and publish their findings.
过去十年中,《自然》《科学》《药理学研究与展望》和其他生物医学研究期刊上的大量文章都强调,研究受到不可靠且无法重现的发现的困扰。实验设计、进行和分析中缺乏充分的统计思维,可能导致部分实验结果不佳。由于统计学家不可能参与每一个临床前实验,因此许多相同的期刊都发布了关于良好统计实践的指南。在这里,我们概述了一种工具,该工具可解决制药行业临床前研究中不可重现性的根本原因。分析能力工具使用 13 个问题来指导科学家和统计学家在体外和体内分析的开发过程中。它促进了绝对必要的实验设计和分析策略,并记录了分析的优势、劣势和精度。然而,与其他提议的解决方案不同的是,它强调了如何使用数据来做出重要决策。可以根据实验数据做出重要决策的置信度,将分析的结果分配为低、中或高的评级。这可以根据分析的当前能力,提供对分析结果的适当解释的透明度。我们建议,在分析开发和使用过程中遵循明确的流程,例如在分析能力工具中规定的流程,这意味着无论结果是阳性还是阴性,研究人员都可以有信心做出决策并公布他们的发现。