论文标题
使用加权回归建模的目标剂量的点和间隔估计
Point and interval estimation of the target dose using weighted regression modelling
论文作者
论文摘要
在通过安慰剂控制的II期剂量调查研究中，将具有多种剂量水平的新药与安慰剂进行比较，以测试新药的有效性。此类研究的主要重点通常在于剂量反应关系的表征，然后估计目标剂量，从而导致对安慰剂的临床相关作用。该靶剂量被称为药物开发研究中的最低有效剂量（MED）。存在几种方法，将多重比较程序与建模技术结合在一起，以有效估计剂量反应模型，然后选择目标剂量。尽管现有方法具有灵活性，但它们仍无法完全解决模型选择步骤中的模型不确定性，并且可能导致目标剂量估计值。在本文中，我们提出了两种基于加权回归模型的新MED估计方法，它们抵抗与剂量反应模型假设的偏差。将这些方法与现有方法的准确性和MED间隔估计有关。我们通过一项模拟研究来说明，通过将一种新的剂量估计方法与现有剂量响应概况估计方法进行整合，可以考虑到模型选择步骤的不确定性。
In a Phase II dose-finding study with a placebo control, a new drug with several dose levels is compared with a placebo to test for the effectiveness of the new drug. The main focus of such studies often lies in the characterization of the dose-response relationship followed by the estimation of a target dose that leads to a clinically relevant effect over the placebo. This target dose is known as the minimum effective dose (MED) in a drug development study. Several approaches exist that combine multiple comparison procedures with modeling techniques to efficiently estimate the dose-response model and thereafter select the target dose. Despite the flexibility of the existing approaches, they cannot completely address the model uncertainty in the model-selection step and may lead to target dose estimates that are biased. In this article, we propose two new MED estimation approaches based on weighted regression modeling that are robust against deviations from the dose-response model assumptions. These approaches are compared with existing approaches with regard to their accuracy in point and interval estimation of the MED. We illustrate by a simulation study that by integrating one of the new dose estimation approaches with the existing dose-response profile estimation approaches one can take into account the uncertainty of the model selection step.
