title: Helping to drive the robustness of preclinical research - the assay capability tool.
abstract: 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.
authors.affiliation: Research Statistics, Clinical Research, Pfizer Cambridge, United Kingdom.
authors.forename: Katrina
authors.initials: K
authors.lastname: Gore
authors.affiliation: Research Statistics, Clinical Research, Pfizer Cambridge, United Kingdom.
authors.forename: Phil
authors.initials: P
authors.lastname: Stanley
keywords: Experimental design; replication; reproducibility; robustness