Speaker
Description
Preclinical research is rich with nontrivial design problems that demand statistical leadership. These opportunities exist across in vivo and high-throughput in vitro research systems where statisticians can materially improve translational fidelity by aligning biological questions, design, and analysis to support decision making. In this talk, I will discuss examples that have arisen from a collaboration focusing on utilising a pooled CRISPR research strategy, which is a technique used to explore library-scale perturbations to analyse the impact of perturbation at a gene level. The potential of this technology, and the complex biological questions being posed, is leading to rapid increase in complexity of the experiments being implemented. A collaboration between researcher and statistician opened with a support request for advice on how many samples researchers needed to design equitable in vivo pooled CRISPR experiments that include male and female samples. To answer this, detective work was needed which uncovered that common statistical pitfalls were rife across the research portfolio and included mis-specified observational/biological/experimental units leading to pseudo-replication, pipelines that ignore blocking and repeated measures, and decision making based on differences in nominal significance error. There is also a need to embrace design complexity to improve generalisability. Together, these issues require a partnership to help the community approach the design and analysis with a robust strategy that is scalable and an evolve to meet the evolution in biological questions being asked. I will share the progress made in embracing hierarchical models with simulations to explore sensitivity and evaluate performance. The aim is to demonstrate the opportunities that can arise within preclinical research for statisticians to design smarter experiments, derive defensible sample sizes, and deliver analyses that move preclinical findings closer to the clinic.
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