Speaker
Description
Preclinical studies often operate under strict ethical, logistical, and financial constraints, resulting in experiments with very small sample sizes. These limitations pose substantial challenges for statistical inference, reproducibility, and the reliability of decision-making in early-phase biomedical research. This talk provides an overview of key design and analysis issues in preclinical experiments, with a particular focus on approaches that help maximize information gain while upholding the principles of reduction and refinement in animal research. After briefly outlining common pitfalls in experimental design, we introduce fundamental considerations for sample size planning in small-scale animal trials, including variance estimation, precision-based approaches, and the role of prior information. Special emphasis is placed on sequential and adaptive designs, which offer flexible interim decision rules, allow for early termination for efficacy or futility, and can substantially reduce the number of animals required without compromising scientific validity. We discuss practical implementation strategies, statistical properties, and typical use cases for sequential methods in preclinical settings.
Optimizing Preclinical Research: Sample Size Planning and Sequential Designs
75002914946