Hierarchical models for tumor xenograft experiments in drug development.
Hierarchical models for tumor xenograft experiments in drug development.
复制标题
药物开发中肿瘤异种移植实验的分层模型。
DOI：
10.1081/bip-200035462
复制
发表时间：
2004
影响因子：
1.1
通讯作者：
Tan,Ming
中科院分区：
文献类型：
--
作者：
Fang,Hong-Bin;Tian,Guo-Liang;Tan,Ming
在抗癌药物开发中，在动物模型上证明抗癌活性是将有前景的化合物推向临床的重要一步。近年来，实验动物实验的合理设计和分析越来越受到人们的重视。这些实验涉及小样本的信息删减的纵向数据。由于对照肿瘤在未经治疗的情况下会发生内在生长，因而存在序约束，使问题进一步复杂化。本文提出了一个贝叶斯层次模型来分析信息删减的纵向数据，同时考虑参数约束并提供有效的小样本推断。我们采用一种非迭代采样方法，即逆贝叶斯公式（IBF）采样器来生成独立的后验样本，从而避免了马尔可夫链蒙特卡罗方法的收敛问题。为了有效地处理受限参数问题，我们使用线性变换简化约束，并利用IBF方法从截断的多元正态分布中生成随机样本。由于使用了弥散先验，后验模式可以很好地近似最大似然估计，因此可以将层次模型视为扩展的混合效应模型。用该方法对一种新的治疗方法的实际异种移植实验进行了分析。
In cancer drug development, demonstrated anticancer activity in animal models is an important step to bring a promising compound to clinic. Proper design and analysis of experiments using laboratory animals have received increasing attention recently. These experiments involve informatively censored longitudinal data with small samples. The problem is further complicated because of order constraints due to the intrinsic growth of control tumors without treatment. This article proposes a Bayesian hierarchical model to analyze informatively censored longitudinal data while accounting for the parameter constraints and providing valid small sample inference. We adopt a noniterative sampling approach,the inverse Bayes formulae(IBF) sampler, to generate independent posterior samples, which avoids convergence problems associated with Markov chain Monte-Carlo methods. To effectively deal with the restricted parameter problem, we use a linear transformation to simplify the constraints and exploit the IBF method to generate random samples from truncated multivariate normal distributions. Because diffuse priors are used, the posterior modes approximate the maximum likelihood estimates well, and the hierarchical model can be considered as an extended mixed-effects model. A real xenograft experiment on a new treatment is analyzed by using the proposed method.