对比连续端点基准剂量计算的实验设计.doc

对比连续端点基准剂量计算的实验设计

摘要
由Crump根据剂量—反应模型提出的BMD（基准剂量）方法被用作对化学物质进行危险度评定。考虑到数据和模型拟合的不确定性，我们把BMD评定的置信下限作为健康风险评估的起始点。本文中，我们将研究如何运用BMD方法获得连续数据的最佳实验设计方法。通过研究幂连续模型来实例说明我们的方法，主要目的是研究提高基准剂量评定条件后，单个剂量组动物数量减少的同时是否需要增加剂量组的数目。由于幂连续模型是非线性模型，所以最佳的设计还要根据未知参数的值来确定。这也是为什么我们认为贝氏设计和假设参数向量有一个先验分布的原因。设计的最主要标准就是要尽量减小BMD评定的预计方差。文中，我们举出了一个例子，例子讲述了几个设计中的设计标准值的计算并试图找出剂量组、剂量组中的动物数量和剂量的选择是如何影响幂连续模型曲线的。从我们的计算中得到，实验中使用四个以上的剂量组会提高取得数据的正确性，从而避免了不利的剂量分配。从而我们也可以断定一些关于预期的剂量—反应曲线的其他信息，例如，从以前的研究中获得的信息，在设计实验时也应该考虑在内，以使实验设计更加精准。

关键词：基准剂量，剂量 - 反应模型，幂连续模型，优化设计
 

Comparing Experimental Designs for Benchmark
Dose Calculations for Continuous Endpoints
Abstract
The BMD (benchmark dose) method that is used in risk assessment of chemical compounds was introduced by Crump and is based on dose-response modeling. To take uncertainty in the data and model fitting into account, the lower confidence bound of the BMD estimate is suggested to be used as a point of departure in health risk assessments. In this article, we study how to design optimum experiments for applying the BMD method for continuous data. We exemplify our approach by considering the Power Continuous models. The main aim is to study whether an increased number of dose groups and at the same time a decreased number of animals in each dose group improves conditions for estimating the benchmark dose. Since Power Continuous models are nonlinear, the optimum design depends on the values of the unknown parameters. That is why we consider Bayesian designs and assume that the parameter vector has a prior distribution. A natural design criterion is to minimize the expected variance of the BMD estimator. We present an example where we calculate the value of the design criterion for several designs and try to find out how the number of dose groups, the number of animals in the dose groups, and the choice of doses affects this value for different Power Continuous curves. It follows from our calculations that to avoid the risk of unfavorable dose placements, it is good to use designs with more than four dose groups.We can also conclude that any additional information about the expected dose-response curve, e.g., information obtained from studies made in the past, should be taken into account when planning a study because it can improve the design.

KEYWORDS: Benchmark dose，dose-response modeling，Power Continuous models，optimum designs

目录
1绪论 1
2方法 3
2.1幂连续模型 3
2.2贝叶斯设计 3
2.3 基准剂量法的设计标准 4
2.4不同设计的比较 7
3结果与讨论 9
3.1斜率参数 的幂连续模型 10
3.2斜率参数 的幂连续模型 11
3.3斜率参数 的幂连续模型 12
3.4斜率参数为 的幂连续模型 13
4一般性讨论 15
5结论 16
致谢 17
参考文献 18
附录 20