Speaker
Description
Translating preclinical results into effective clinical treatments remains a big challenge in biomedical research. Too often, findings from single-laboratory studies fail to replicate in the preclinical context and further, to show effectiveness in clinical trials. One promising approach to validate exploratory findings is through confirmatory multi-laboratory preclinical trials—studies that test the same intervention across several independent labs using harmonized protocols, in a similar way that randomized-controlled clinical trials (RCT). Given the complexity of the experimental design, these trials tend to adopt rigorous experimental practices such as sample size calculation and randomization of animals into treatment groups, which provide robust and reliable evidence to decide whether to move forward into clinical trials or not.
The added complexity comes with a major challenge: how to plan, analyse, and report these studies properly. In this talk, general recommendations will outline how to plan the analysis and how to report multi-laboratory preclinical studies. The recommendations focus on key items to include in the statistical analysis plan (SAP) drawing from the experimental design and rigorous research practices, and considering statistical principles. Ultimately, the goal is to raise awareness on practical and statistical considerations, and encourage a closer exchange between experimental researchers and statisticians to perform decision-enabling preclinical studies.
64288204088