LAUSR

INTRODUCTION: Randomization is a cornerstone of controlled studies such as mouse clinical trials (MCT). However, simple randomization schemes often lead to unbalanced group allocations, therefore reducing the statistical power of experimental studies. This issue becomes more relevant and critical in MCTs where it is usually necessary to allocate a large number of mice into multiple groups/arms, so that all groups have close tumor volumes (measured by average and variance) as well as other covariates such as body weight, while at the same time also using as few mice as possible. Possible allocations grow exponentially by mouse and group number, and it is computationally intractable to identify optimal allocation schemes; instead, heuristic methods are used to search for near-optimal solutions. Studylog® systems can balance tumor volume with at most one baseline covariate. Randmice, a newly developed tool by Stimunity, shows good performance in balancing bilateral tumors, but it cannot balance more than two baseline covariates. We have developed a fast, in-house, online tool to balance any number of baseline covariates and, in addition, users can assign different weights to each baseline covariate based on levels of importance. METHODS: We used a two-step method to balance designated baseline covariates for MCTs. First, we used a matched algorithm to find sub-matches from a population of mice, by grouping together mice with similar values of baseline covariates. Based on these sub-matches, we then used an optimization method to minimize the imbalance of baseline covariates guided by a weighted score function. Once the optimization was complete, a summary statistic table and a diagnostic plot for the top 10 group allocation solutions were displayed for picking the most suitable solution. RESULTS: We used our baseline covariate balancing tool for a benchmark set of mouse clinical trials with more than one baseline covariate to be balanced out, with the difference of key baseline covariates among groups well under control with close means. The tool completed the allocations in a few minutes by running on a single-CPU computer. CONCLUSIONS: We have developed a fast and powerful online tool to help preclinical researchers balance key baseline covariates in their studies, particularly in mouse clinical trials. Citation Format: Binchen Mao, Davy Ouyang, Henry Li, Sheng Guo. An online tool to balance baseline covariates for mouse clinical trials [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3228.