Stead A G, Hasselblad V, Creason J P, Claxton L
Mutat Res. 1981 Feb;85(1):13-27. doi: 10.1016/0165-1161(81)90282-x.
Despite the value and widespread use of the Ames test, little attention has been focused on standardizing quantitative methods of analyzing these data. In this paper, a realistic and statistically tractable model is developed for the evaluation of Ames-type data. The model assumes revertant colony formation at any dose follows a Poisson process, while the mean number of revertants per plate is a nonlinear function of up to 4 parameters. An exponential decay term can be included in the model to adjust for toxicity. The resultant system of nonlinear equations is solved using a modified Gauss-Newton iterative scheme to obtain maximum likelihood estimates of the model parameters. Significance of the key parameters is tested by fitting reduced models and using likelihood ratio tests. The model's performance is demonstrated on data from organic extracts of various environmental contaminants. Among the advantages of the proposed model are (1) no data is discarded in the parameter estimation process, (2) no arbitrary constants need to be added to zero counts or doses, and (3) no mathematical transformation of the data is required.
尽管艾姆斯试验具有价值且被广泛使用，但很少有人关注分析这些数据的定量方法的标准化。本文针对艾姆斯试验类型的数据评估开发了一个现实且易于进行统计分析的模型。该模型假设在任何剂量下回复突变菌落的形成遵循泊松过程，而每平板回复突变体的平均数是多达4个参数的非线性函数。模型中可包含一个指数衰减项以调整毒性。使用改进的高斯 - 牛顿迭代法求解所得的非线性方程组，以获得模型参数的最大似然估计值。通过拟合简化模型并使用似然比检验来检验关键参数的显著性。该模型的性能在各种环境污染物有机提取物的数据上得到了验证。所提出模型的优点包括：（1）在参数估计过程中不丢弃任何数据；（2）无需对零计数或剂量添加任意常数；（3）无需对数据进行数学变换。
