Statistical Methods for Multi-Chemical Toxicity Studies

多种化学品毒性研究的统计方法

基本信息

批准号：
8553798
负责人：
Gregg Dinse
金额：
$ 29.15万
依托单位：
NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

项目摘要

The majority of my research on this project was performed in five areas: (1) generalizing the concept of relative potency, (2) developing statistical methods for making inferences about generalized relative potency functions, (3) specifying a power model to express relative potency as a function of dose, (4) studying methods that rely on dose additivity, and (5) identifying situations in which Hill model parameters are not uniquely estimable. These five areas of research are described in more detail below. Area 1: Relative potency plays an important role in toxicology. Estimates of relative potency are used to rank chemicals by their effects, to calculate equivalent doses of test chemicals compared to a standard, and to weight contributions of constituent chemicals when evaluating mixtures. Within a class of chemicals having "similar" dose-response curves, the relative potency of one chemical compared to another is the ratio of doses producing the same toxicity response, and this ratio is constant across all levels of response. If the dose-response curves are non-similar, however, relative potency need not be constant and typically varies according to where along the dose-response curves the dose ratio is calculated. In practice, relative potency is usually equated to a constant dilution factor, even when non-similar dose-response curves indicate that constancy is inappropriate. Improperly regarding relative potency as constant may distort conclusions and potentially mislead investigators or policymakers. We developed a more general approach that allows relative potency to vary as a function of the dose of a chemical, the level of a specific response, or the percentage of the range of possible response levels. Distinct functions can be defined, each generalizing different but equivalent descriptions of constant relative potency. These relative potency functions are constructed from dose-response curves for test and reference chemicals, and they all provide fundamentally equivalent information if the chemicals have the same lower and upper limits of response. In fact, if two chemicals differ only with respect to their ED50s (i.e., their dose-response curves are similar), then all of the relative potency functions are constant and equal to the ratio of the ED50s. Otherwise, if the response limits differ, relative potency as a function of the response-range percentage is distinct from the other functions and embodies a modified definition of relative potency. Non-constant relative potency functions may cross the baseline value of 1.0, indicating that one chemical is more potent than another for some doses, responses, or response-range percentages. If chemicals have non-similar dose-response curves, then inferences based on ratios of ED50s or based on models that force the other parameters to be identical can be misleading. Thus, we recommend using relative potency functions, where the preferred function depends on the application (e.g., chemical ranking or dose conversion) and whether one views differences in response limits as intrinsic to the chemicals or as extrinsic, arising from idiosyncrasies of data sources. Relative potency functions offer a unified and principled description of relative potency for non-similar dose-response curves. We recently wrote a book chapter on this topic, which is currently under review. Also, I gave an invited talk about this research at the Conference on Risk Assessment and Evaluation of Predictions in Silver Spring, MD on October 13, 2011. Area 2: In ongoing research, we are working on formal statistical methods for analyzing relative potency functions. First, we will describe techniques for estimating parameters in the underlying dose-response models (e.g., Hill models), assessing model adequacy, quantifying variability of parameter estimates, constructing confidence intervals for model parameters, and testing hypotheses about model parameters. Then, based on specific models for the dose-response curves, we will develop procedures for making inferences about the resulting relative potency functions and any summaries obtained from these functions. These procedures will deal with function estimation, variance estimation, construction of pointwise confidence intervals and simultaneous confidence bands, and testing of hypotheses. Area 3: Recently, we developed an approach that parameterizes dose-response models in a way that enables relative potency functions to be estimated directly. For example, when analyzing data on a reference chemical and a test chemical, rather than specifying models for both dose-response functions and then using these to derive a formula for the relative potency function, we specified a dose-response model for the reference chemical and a relative potency model. This approach provides direct estimates of the relative potency function (and indirect estimates of the dose-response function for the test chemical). In particular, we used a power function in dose as a relative potency model, which is equivalent to a log-linear model (in dose) for the log relative potency function. This model keeps the dose-response functions for the two chemicals within the same family of models for those models typically used in toxicology. When differences in the response limits for the test and reference chemicals are attributable to the chemicals themselves, the indirect approach is the more convenient. When differences in response limits are attributable to other features of the experimental protocol or when response limits do not differ, our new direct approach is straightforward to apply with nonlinear regression methods and simplifies calculation of simultaneous confidence bands. We published an article describing this work (see reference 1). Area 4: Analyses of chemical mixtures often rely on an assumption of dose additivity. Under this assumption, each test chemical can be expressed in terms of equivalent units of a reference chemical, which allows a dose of some mixture of these chemicals to be expressed as a weighted sum of the doses of the constituent chemicals. For much of the past year, a small working group has been reading papers on dose additivity and studying various methods that assume dose additivity. This group is composed of four researchers: Cynthia Rider (NTP), Jane Ellen Simmons (EPA), David Umbach (BB), and myself. We meet for a few hours every two or three weeks; we discuss the papers assigned at the last meeting; and we work towards a comprehensive review paper on dose additivity. This work is ongoing and we hope to have a draft manuscript completed by late 2012. Area 5: Finally, I have also been studying ways to determine the number of uniquely estimable parameters when a Hill model is fitted to binary data. It is well known that there are identifiability problems if all or none of the subjects respond. Similar problems arise if all subjects receiving a dose below a certain level do not respond and all subjects receiving a higher dose do respond. On the other hand, if there are several observed response rates and they tend to be ordered with respect to dose, then typically the Hill model parameters are uniquely estimable. For intermediate cases, however, the number of uniquely estimable parameters appears to be related to the number of distinct nonparametric estimates obtained under a monotonicity constraint on the dose-response curve (i.e., the number of level sets in a nonparametric isotonic regression analysis).

我对该项目的大部分研究是在五个领域进行的：（1）概括相对效力的概念，（2）开发统计方法来推断有关广义相对效能功能的推断，（3）指定功率模型来表达相对效力，以剂量的函数，（4）研究依赖剂量添加性和（5）估计山上模型的研究方法，该方法不合时间是shill模型。下面更详细地介绍了这五个研究领域。区域1：相对效力在毒理学中起重要作用。相对效力的估计值用于通过其作用对化学品进行对，以计算与标准品相比的等效剂量的测试化学物质，以及评估混合物时组成化学物质的权重贡献。在具有“相似”剂量反应曲线的化学物质中，与另一种化学物相比，一种化学物质的相对效力是产生相同毒性反应的剂量的比率，并且在所有水平的响应中，该比率均保持恒定。但是，如果剂量反应曲线是非相似的，则相对效力不必恒定，并且通常根据沿剂量反应曲线的位置而变化，则计算剂量比。实际上，即使非相似剂量反应曲线表明恒定性不合适，相对效力通常等于恒定稀释因子。关于恒定的相对效力不当可能会扭曲结论，并可能误导研究人员或决策者。我们开发了一种更通用的方法，该方法允许相对效力随着化学剂量的函数而变化，特定响应水平或可能响应水平的范围的百分比。可以定义不同的功能，每个功能都概括了恒定相对效力的不同但等效的描述。这些相对效力函数是由用于测试和参考化学物质的剂量响应曲线构建的，如果化学物质具有相同的下层和上限响应，它们都提供了等效的信息。实际上，如果两种化学物质仅在其ED50上有所不同（即它们的剂量反应曲线相似），则所有相对效力函数均恒定并且等于ED50的比率。否则，如果响应限制差异，则相对效力与响应范围百分比的函数不同于其他功能，并体现了对相对效力的修改定义。非恒定相对效力函数可能会超过1.0的基线值，这表明一种化学物质对于某些剂量，响应或响应范围的百分比更有效。如果化学物质具有非相似剂量反应曲线，则基于ED50的比率或基于迫使其他参数相同的模型的推论可能会误导。因此，我们建议使用相对效力函数，其中首选函数取决于应用（例如化学排名或剂量转换），以及一个人认为响应限制的差异是化学物质固有的还是外在的，这是由数据源的特质引起的。相对效能功能提供了对非相似剂量响应曲线相对效力的统一和原则描述。我们最近撰写了有关此主题的书章，目前正在审查中。另外，我在2011年10月13日在马里兰州银春的风险评估和预测评估会议上发表了有关这项研究的邀请。区域2：在正在进行的研究中，我们正在研究用于分析相对效力功能的形式统计方法。首先，我们将描述用于估算基础剂量响应模型（例如山丘模型）中参数的技术，评估模型是否足够，量化参数估计值的可变性，为模型参数构建置信区间以及对模型参数的测试假设。然后，基于剂量响应曲线的特定模型，我们将开发有关推断产生的相对效力函数以及从这些功能获得的任何摘要的过程。这些过程将处理功能估计，方差估计，置换置信区间的构建和同时置信频段以及假设的测试。区域3：最近，我们开发了一种方法，该方法以一种可以直接估算相对效力函数的方式来参数化剂量反应模型。例如，在分析参考化学和测试化学化学的数据时，而不是为两个剂量反应函数指定模型，然后使用它们来得出相对效力函数的公式，我们为参考化学物质和相对效力模型指定了剂量反应模型。这种方法提供了对相对效能函数的直接估计（以及测试化学剂量反应函数的间接估计）。特别是，我们将剂量中的功率函数用作相对效力模型，该模型等于对数相对效力函数的对数线性模型（剂量）。该模型将两种化学物质的剂量反应函数保留在同一模型家族中，用于毒理学通常使用的那些模型。当测试和参考化学物质的响应限制的差异归因于化学物质本身时，间接方法越方便。当响应限制的差异归因于实验协议的其他特征或响应限制没有差异时，我们的新直接方法是直接适用于非线性回归方法的，并简化了同时置信频段的计算。我们发表了一篇描述这项工作的文章（请参阅参考文献1）。区域4：化学混合物的分析通常取决于剂量添加性的假设。在此假设下，每种测试化学物质都可以用参考化学物质的等效单位表示，这使得这些化学物质的某些混合物可以表示为成分化学物质剂量的加权总和。在过去的一年中的大部分时间里，一个小型工作组一直在阅读有关剂量添加性的论文，并研究了采用剂量添加性的各种方法。该小组由四个研究人员组成：辛西娅·骑手（NTP），简·艾伦·西蒙斯（EPA），大卫·乌姆巴赫（BB）和我本人。我们每两三个星期开会几个小时；我们讨论了上次会议上分配的论文；我们正在努力综合有关剂量添加性的综合审查论文。这项工作正在进行中，我们希望在2012年底之前完成手稿草案。区域5：最后，当将山坡模型拟合到二进制数据时，我还一直在研究确定可估计参数的数量的方法。众所周知，如果所有受试者都反应，存在可识别性问题。如果所有接受剂量以下剂量以下的受试者没有反应，并且所有接受较高剂量的受试者都会做出反应，则会出现类似的问题。另一方面，如果有几个观察到的响应率，并且倾向于对剂量进行排序，则通常可以唯一估计山丘模型参数。但是，对于中间情况，可唯一估计的参数的数量似乎与在剂量反应曲线上单调性约束下获得的不同非参数估计的数量有关（即，非参数同位素回归分析中的水平集数量）。