Tan Ming, Fang Hong-Bin, Tian Guo-Liang, Houghton Peter J
Department of Biostatistics, St Jude Children's Research Hospital, Memphis, Tennessee 38105, USA.
Biometrics. 2002 Sep;58(3):612-20. doi: 10.1111/j.0006-341x.2002.00612.x.
In cancer drug development, demonstrating activity in xenograft models, where mice are grafted with human cancer cells, is an important step in bringing a promising compound to humans. A key outcome variable is the tumor volume measured in a given period of time for groups of mice given different doses of a single or combination anticancer regimen. However, a mouse may die before the end of a study or may be sacrificed when its tumor volume quadruples, and its tumor may be suppressed for some time and then grow back. Thus, incomplete repeated measurements arise. The incompleteness or missingness is also caused by drastic tumor shrinkage (<0.01 cm3) or random truncation. Because of the small sample sizes in these models, asymptotic inferences are usually not appropriate. We propose two parametric test procedures based on the EM algorithm and the Bayesian method to compare treatment effects among different groups while accounting for informative censoring. A real xenograft study on a new antitumor agent, temozolomide, combined with irinotecan is analyzed using the proposed methods.
在癌症药物研发中，在异种移植模型（将人类癌细胞移植到小鼠体内）中证明药物活性，是将一种有前景的化合物应用于人体的重要一步。一个关键的结果变量是在给定时间段内，给予不同剂量单一或联合抗癌方案的小鼠组的肿瘤体积。然而，小鼠可能在研究结束前死亡，或者当肿瘤体积增大四倍时被处死，并且其肿瘤可能在一段时间内受到抑制，然后又重新生长。因此，出现了不完全重复测量的情况。这种不完全性或缺失性也是由肿瘤急剧缩小（<0.01 cm³）或随机截断导致的。由于这些模型中的样本量较小，渐近推断通常并不合适。我们提出了两种基于期望最大化（EM）算法和贝叶斯方法的参数检验程序，在考虑信息删失的情况下比较不同组之间的治疗效果。使用所提出的方法分析了一项关于新型抗肿瘤药物替莫唑胺联合伊立替康的真实异种移植研究。
