Multivariate Bayesian Logistic Regression for Analysis of Clinical Study Safety Issues
Multivariate Bayesian Logistic Regression for Analysis of Clinical Study Safety Issues
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DOI：
10.1214/11-sts381
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发表时间：
2012-08-01
影响因子：
5.7
通讯作者：
DuMouchel, William
中科院分区：
文献类型：
--
作者：
DuMouchel, William
本文描述了一种基于模型的临床安全性数据分析方法，称为多元贝叶斯逻辑回归 (MBLR)。并行逻辑回归模型适合一组医学相关问题或响应变量，MBLR 允许来自不同问题的信息相互“借用力量”。该方法特别适合稀疏的反应数据，这种情况经常发生在从研究对象中收集细粒度不良事件时，这些研究的规模更大，以有效性而非安全性调查为目的。可以对多项研究的数据进行组合分析，并且该方法能够根据回归模型中的协变量搜索易受影响的亚组。提供了一个涉及 8 项研究中的 10 个医学相关问题的示例，以及显示该方法的分布特性的模拟。
This paper describes a method for a model-based analysis of clinical safety data called multivariate Bayesian logistic regression (MBLR). Parallel logistic regression models are fit to a set of medically related issues, or response variables, and MBLR allows information from the different issues to "borrow strength" from each other. The method is especially suited to sparse response data, as often occurs when fine-grained adverse events are collected from subjects in studies sized more for efficacy than for safety investigations. A combined analysis of data from multiple studies can be performed and the method enables a search for vulnerable subgroups based on the covariates in the regression model. An example involving 10 medically related issues from a pool of 8 studies is presented, as well as simulations showing distributional properties of the method.