Abstract 2829: Strategies to overcome challenges in randomization and grouping methodologies in in vivo studies using the power of animal study workflow software

Abstract

Abstract Preclinical research in Oncology requires using a wide variety of animal models. Robust, clinically relevant distribution methods including randomized and non-random methods appropriate to the model are essential to ensure unbiased group assignments and reliable experimental outcomes. This presentation will explore flaws and inconveniences in prevalent randomization methods and strategies to overcome them using animal study workflow software and demonstrate the power of each with unique examples. Strategies for randomizing individual animals into groups and randomizing mouse cages into groups will be explored. Strategies for exclusion criteria, robust documentation, multi-parameter randomization, re-randomization, and clinical relevance of these strategies will be discussed. Standardization and automation of distribution methods help improve process integrity, research quality, and reproducibility. Citation Format: Raghu Ramachandra. Strategies to overcome challenges in randomization and grouping methodologies in in vivo studies using the power of animal study workflow software [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2829.