\u8b1b\u00a0 \u984c\uff1aStatistical considerations and performance evaluation in mouse tumor models<\/p>\n
\u4e3b\u8b1b\u4eba\uff1a\u9673\u66c9\u5029 \u52a9\u7406\u6559\u6388 (Department of Biostatistics, Vanderbilt University Medical Center)<\/p>\n
\u6642\u00a0 \u9593\uff1a2025\u5e7406\u670812\u65e5\uff08\u661f\u671f\u56db\uff09\u4e0b\u534802\uff1a10 – 04\uff1a00<\/p>\n
\u5730\u00a0 \u9ede\uff1aB302A\uff08\u6de1\u6c34\u6821\u5712\u5546\u7ba1\u5927\u6a13\uff09<\/p>\n
\u8336\u00a0 \u6703\uff1a2025\u5e7406\u670812\u65e5\uff08\u661f\u671f\u56db\uff09\u4e0b\u534801\uff1a30 (\u5546\u7ba1\u5927\u6a13 B1102)<\/p>\n
Mouse tumor volume measurements in preclinical studies are typically collected at regular intervals (equally spaced) or at least twice weekly (unequally spaced). A commonly used summary metric to evaluate drug efficacy is the treatment-to-control (T\/C) ratio, which compares tumor volumes in treated (T) versus control (C) groups at a specific time point (Wu, 2010; Hather et al., 2014). Traditional statistical approaches, such as the two-sample t-test (a special case of ANOVA) or the Wilcoxon rank-sum test, are often applied at selected time points to assess treatment effects.<\/p>\n
In this study, we compare the performance of several statistical methods for analyzing tumor volume data from xenograft experiments. Specifically, we evaluate both ratio-based T\/C methods (tumor volume ratio) and rate-based T\/C methods (tumor growth rate ratio), alongside the rate-based t-test, independent two-sample t-test, linear regression, and mixed-effects models. Simulated datasets were generated using a linear mixed-effects model, with parameters informed by two real-world tumor growth models.<\/p>\n
Our evaluation focuses on Type I error rate, statistical power, and coverage probability for detecting treatment effects at a 0.05 significance level. Results indicate that ratio-based T\/C methods tend to inflate Type I error rates. Mixed-effects models demonstrate robust performance when their assumptions are met and outperform the other methods. However, the rate-based t-test may serve as an alternative when sample sizes are sufficiently large, as determined through power analysis in consultation with a statistician.<\/p>\n