GENERALIZATION OF PROBIT AND LOGIT METHODS FOR DOSE-RESPONSE CURVES
GENERALIZATION OF PROBIT AND LOGIT METHODS FOR DOSE-RESPONSE CURVES
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DOI：
10.2307/2529262
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发表时间：
1976-01-01
期刊：
影响因子：
1.9
通讯作者：
PRENTICE, RL
中科院分区：
文献类型：
--
作者：
PRENTICE, RL
用4参数类建立了量子响应生物测定中响应概率与剂量的关系模型。除了位置和尺度量外，该模型还包括2个形状参数，基本上指示剂量-反应曲线尾部的偏斜度和沉重度。这类模型包括逻辑分布函数、正态分布函数、极端最小值分布函数、极端最大值分布函数、双指数分布函数、指数分布函数和反射指数分布函数等特殊情况。得分测试来自逻辑和正常假设和某些子模型进行了讨论，模型拟合计算方便。布利斯的数据说明了在估计临界剂量水平方面，与通常方法相比，可能会有改进。
The relationship between response probability and dosage in quantal response bioassay is modeled using a 4 parameter class. In addition to location and scale quantities the model includes 2 shape parameters that essentially index skewness and heaviness of tails of the dose-response curve. The class of models includes such special cases as the logistic, normal, extreme minimum value, extreme maximum value, double exponential, exponential and reflected exponential distribution functions. Score tests are derived for logistic and normal hypotheses and certain submodels are discussed for which the model fitting is computationally convenient. The data of Bliss illustrates the potential improvement over usual methods in the estimation of critical dose levels.