Combining multiple comparisons and modeling techniques in dose-response studies
Combining multiple comparisons and modeling techniques in dose-response studies
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DOI：
10.1111/j.1541-0420.2005.00344.x
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发表时间：
2005-09-01
期刊：
影响因子：
1.9
通讯作者：
Branson, M
中科院分区：
文献类型：
--
作者：
Bretz, F;Pinheiro, JC;Branson, M
长期以来，对剂量反应研究数据的分析分为两种主要策略：多重比较程序和基于模型的方法。基于模型的方法假设反应和剂量之间的函数关系，根据预先指定的参数模型，将其作为一个定量因素。然后使用拟合的模型来估计达到预期反应的适当剂量，但其结论的有效性将在很大程度上取决于先验未知剂量-反应模型的正确选择。多重比较程序将剂量视为一个定性因素，并对潜在的剂量-反应模型做出极少的假设(如果有的话)。主要目标往往是确定具有统计意义并产生相关生物效应的最小有效剂量。一种方法是评估不同剂量水平之间对比的重要性，同时保持家庭误差率。这种程序相对稳健，但推断仅限于在所调查的剂量水平中选择目标剂量。我们描述了一个统一的策略来分析来自剂量反应研究的数据，它结合了多种比较和建模技术。我们假设存在几个候选的参数模型，并使用多种比较技术来选择最可能代表真实潜在剂量-反应曲线的模型，同时保持家庭误差率。然后，选择的模型被用来提供关于适当剂量的推断。
The analysis of data from dose-response studies has long been divided according to two major strategies: multiple comparison procedures and model-based approaches. Model-based approaches assume a functional relationship between the response and the dose, taken as a quantitative factor, according to a prespecified parametric model. The fitted model is then used to estimate an adequate dose to achieve a desired response but the validity of its conclusions will highly depend on the correct choice of the a priori unknown dose-response model. Multiple comparison procedures regard the dose as a qualitative factor and make very few, if any, assumptions about the underlying dose-response model. The primary goal is often to identify the minimum effective dose that is statistically significant and produces a relevant biological effect. One approach is to evaluate the significance of contrasts between different dose levels, while preserving the family-wise error rate. Such procedures are relatively robust but inference is confined to the selection of the target dose among the dose levels under investigation. We describe a unified strategy to the analysis of data from dose-response studies which combines multiple comparison and modeling techniques. We assume the existence of several candidate parametric models and use multiple comparison techniques to choose the one most likely to represent the true underlying dose-response curve, while preserving the family-wise error rate. The selected model is then used to provide inference on adequate doses.