The translation of preclinical studies to human applications is associated with a high failure rate, which may be exacerbated by limited training in experimental design and statistical analysis. Nested experimental designs, which occur when data have a multilevel structure (e.g., in vitro: cells within a culture dish; in vivo: rats within a litter), often violate the independent observation assumption underlying many traditional statistical techniques. Although previous studies have empirically evaluated the analytic challenges associated with multilevel data, existing work has not focused on key parameters and design components typically observed in preclinical research. To address this knowledge gap, a Monte Carlo simulation study was conducted to systematically assess the effects of inappropriately modeling multilevel data via a fixed effects ANOVA in studies with sparse observations, no between group comparison within a single cluster, and interactive effects. Simulation results revealed a dramatic increase in the probability of type 1 error and relative bias of the standard error as the number of level-1 (e.g., cells; rats) units per cell increased in the fixed effects ANOVA; these effects were largely attenuated when the nesting was appropriately accounted for via a random effects ANOVA. Thus, failure to account for a nested experimental design may lead to reproducibility challenges and inaccurate conclusions. Appropriately accounting for multilevel data, however, may enhance statistical reliability, thereby leading to improvements in translatability. Valid analytic strategies are provided for a variety of design scenarios.

中文翻译：

---

有效的聚类数据统计方法：蒙特卡洛模拟研究

临床前研究向人类应用的转化会导致高失败率，而实验设计和统计分析方面的有限培训可能会加剧这种失败率。嵌套的实验设计，当数据具有多层结构（例如，体外：培养皿中的细胞；体内：窝里的老鼠），经常违反许多传统统计技术所基于的独立观察假设。尽管以前的研究已经从经验上评估了与多级数据相关的分析挑战，但是现有工作并未集中于临床前研究中通常观察到的关键参数和设计组件。为了解决这一知识鸿沟，进行了蒙特卡罗模拟研究，以通过固定效应方差分析对具有稀疏观测的研究（不适用于单个聚类中的组间比较和交互效应）进行系统评估，不适当地建模了多级数据。仿真结果表明，随着1级（例如，单元格，大鼠）固定效应方差分析中每个细胞的单位增加; 当通过随机效应方差分析对嵌套进行适当说明时，这些效应会大大减弱。因此，无法解释嵌套的实验设计可能导致可重复性挑战和不正确的结论。但是，适当考虑多级数据可能会增强统计的可靠性，从而导致可翻译性的提高。为各种设计方案提供了有效的分析策略。从而改善了可翻译性。为各种设计方案提供了有效的分析策略。从而改善了可翻译性。为各种设计方案提供了有效的分析策略。