Section of Biostatistics and Epidemiology, Dartmouth Medical School, Hanover, New Hampshire 03755, USA.
Int J Radiat Biol. 2010 Feb;86(2):164-73. doi: 10.3109/09553000903419304.
To review the existing endpoints of tumour growth delay assays in experimental radiobiology with an emphasis on their efficient estimation for statistically significant identification of the treatment effect. To mathematically define doubling time (DT), tumour-growth delay (TGD) and cancer-cell surviving fraction (SF) in vivo using exponential growth and regrowth models with tumour volume measurements obtained from animal experiments.
A statistical model-based approach is used to define and efficiently estimate the three endpoints of tumour therapy in experimental cancer research.
The log scale is advocated for plotting the tumour volume data and the respective analysis. Therefore, the geometric mean should be used to display the mean tumour volume data, and the group comparison should be a t-test for the log volume to comply with the Gaussian-distribution assumption. The relationship between cancer-cell SF, TGD and rate of growth is rigorously established. The widespread formula for cell kill is corrected; it has been rigorously shown that TGD is the difference between DTs. The software for the tumour growth delay analysis based on the mixed modeling approach with a complete set of instructions and example can be found on the author's webpage.
The existing practice for TGD data analysis from animal experiments suffers from imprecision and large standard errors that yield low power and statistically insignificant treatment effect. This practice should be replaced with a model-based statistical analysis on the log scale.
回顾实验放射生物学中肿瘤生长延迟测定的现有终点，并特别强调其对治疗效果的统计学显著性识别的有效估计。使用基于数学模型的方法，通过动物实验中获得的肿瘤体积测量值，在体内使用指数增长和再增长模型来定义倍增时间（DT）、肿瘤生长延迟（TGD）和癌细胞存活分数（SF）。
采用基于统计模型的方法来定义和有效地估计实验性癌症研究中肿瘤治疗的三个终点。
建议在对数尺度上绘制肿瘤体积数据和相应的分析。因此，应使用几何平均值来显示平均肿瘤体积数据，并且为了符合正态分布假设，应使用对数体积进行组间比较 t 检验。严格建立了癌细胞 SF、TGD 和生长速率之间的关系。修正了广泛使用的细胞杀伤公式；已经严格证明 TGD 是 DT 的差异。基于混合建模方法的肿瘤生长延迟分析软件，带有完整的使用说明和示例，可以在作者的网页上找到。
目前从动物实验中分析 TGD 数据的实践存在不精确性和较大的标准误差，导致功效低且统计学上无显著的治疗效果。这种做法应该被基于对数尺度的基于模型的统计分析所取代。
