Sidik Kurex, Jonkman Jeffrey N
a Bristol-Myers Squibb Company , Princeton , New Jersey , USA.
b Department of Mathematics & Statistics , Grinnell College , Grinnell , Iowa , USA.
J Biopharm Stat. 2016;26(2):250-68. doi: 10.1080/10543406.2014.1003432. Epub 2015 Jan 28.
For bioassay data in drug discovery and development, it is often important to test for parallelism of the mean response curves for two preparations, such as a test sample and a reference sample in determining the potency of the test preparation relative to the reference standard. For assessing parallelism under a four-parameter logistic model, tests of the parallelism hypothesis may be conducted based on the equivalence t-test or the traditional F-test. However, bioassay data often have heterogeneous variance across dose levels. Specifically, the variance of the response may be a function of the mean, frequently modeled as a power of the mean. Therefore, in this article we discuss estimation and tests for parallelism under the power variance function. Two examples are considered to illustrate the estimation and testing approaches described. A simulation study is also presented to compare the empirical properties of the tests under the power variance function in comparison to the results from ordinary least squares fits, which ignore the non-constant variance pattern.
在药物发现与开发中的生物测定数据方面，检测两种制剂（如测试样品和参比样品）的平均响应曲线的平行性通常很重要，这有助于在确定测试制剂相对于参比标准的效价时进行比较。对于在四参数逻辑模型下评估平行性，平行性假设的检验可基于等效t检验或传统的F检验来进行。然而，生物测定数据在不同剂量水平上往往具有异质性方差。具体而言，响应的方差可能是均值的函数，通常建模为均值的幂次。因此，在本文中，我们讨论了在幂次方差函数下平行性的估计和检验。通过两个例子来说明所描述的估计和检验方法。还进行了一项模拟研究，以比较幂次方差函数下检验的经验性质与忽略方差非恒定模式的普通最小二乘法拟合结果。
